Navigating the Student Journey: A Comprehensive Guide to GTPs and Resources

Starting your college career can be daunting, but the Huntington University Registrar's Office is here to make things simpler. The office serves as a central hub for all your academic needs, from accessing official college records and transcripts to finding crucial course information.

Essential Resources for Students

For prospective students still exploring college options, Huntington University offers several programs to enrich their academic experience:

Early Entry Program: Allows high school students to get a head start on their college education.
Advanced Placement Credit Options: Enables students to earn college credit for qualifying AP exam scores.
Customized Academic Program (CAP): Provides a flexible framework for students to tailor their academic path to their specific interests and goals.
Study Abroad Program: Offers opportunities to gain international experience and broaden their perspectives.

If you are interested in transferring, check out the next steps you need to take to attend HU. Also, this form will get you started on transferring credits.

Guide to Typical Programs (GTPs)

For students who like to plan, Huntington University provides a valuable tool known as Guide to Typical Programs (GTPs). Each major has a GTP.

Understanding Trauma in Geriatric Patients (GTPs)

Predicting unfavorable outcomes in geriatric trauma patients (GTPs) remains a significant challenge, as trauma remains a leading cause of mortality in this population. A retrospective cohort study included trauma patients ≥ 65 years from July 29, 2016 to September 19, 2024. The GERtality score, Geriatric Trauma Outcome Score (GTOS), GTOS II, Trauma Injury Severity Score (TRISS), and adjusted TRISS (aTRISS) were calculated for each patient. The statistical performance of these scores was evaluated using the concordance statistic (C-index), and calibration was assessed by comparing observed versus predicted mortality risks through calibration plots.

Of 1,081 GTPs, a total of 86 (7.9%) required MV, and 93 individuals (8.6%) were deceased during hospitalization. The GERtality score (C-index = 0.89, 95% CI: 0.85 to 0.93) and GTOS (C-index = 0.86, 95% CI: 0.84 to 0.93) demonstrated the highest predictive value for in-hospital mortality. For MV, GERtality and GTOS exhibited the highest predictive performance (C-index = 0.82). The calibration slopes for all scoring systems were close to 1.0 (range: 0.97 to 0.99), with confidence intervals including 1.0, indicating good agreement between predicted and observed mortality risks.

Trauma and its associated complications account for approximately 8% of all-cause mortality worldwide, with an estimated 4.4 million trauma-related deaths reported. While the incidence of traumatic injuries is higher among the young population, there is a concerning rise trend in trauma incidence among the geriatric population. This growing trend is primarily caused by demographic changes, including an aging population and susceptibility to specific injuries, such as falling among this group. Notably, traumatic mortality rates for trauma in individuals aged 65 and older are reported as 31-61% higher than those observed in younger patients.

There are several risk factors in geriatric trauma patients (GTPs), including preexisting cardiovascular and respiratory diseases, renal failure, and poor nutritional status, which contribute to their increased morbidity and mortality. A fundamental component of trauma management is the rapid and accurate assessment of patients’ clinical status on arrival at a trauma center, which guides critical medical decisions. Trauma scoring systems assist emergency department physicians in this process. Given the significant influence of age on trauma outcomes, a number of scoring systems have been developed explicitly for use in specific age groups, such as the GTPs. Recent studies worldwide have validated the prognostic utility of geriatric-specific trauma scores in predicting in-hospital outcomes.

Study on Trauma Scoring Systems

A retrospective cohort study was conducted on trauma patients aged ≥ 65 referred to Sina Hospital, Tehran, Iran, from 29 July 2016 to 19 September 2024. The study was conducted within the framework of the National Trauma Registry of Iran (NTRI), and patients were identified through this registry. As a result, all patients meet the registry’s inclusion criteria, including hospitalization for more than 24 h, deceased patients within the first 24 h of hospitalization, or patients transferred from the intensive care unit (ICU) of another hospital to the current hospital’s ICU, with a length of stay of less than 24 h. The data for this study were collected using two approaches: the NTRI database and the targeted extraction of specific data from patient records.

The registry process involves identifying patients with traumatic injuries based on International Classification of Diseases, Tenth Revision (ICD-10) codes and registering those who meet specific criteria (e.g., hospital stay over 24 h, death, or ICU transfer). Trained registrars, usually nurses, collect comprehensive data (99 variables) via bedside interviews, physical exams, and hospital records, which are uploaded to a web-based portal. To ensure data accuracy, trained reviewers and a surgeon controller carefully assess the collected information, adhering to established guidelines for injury severity assessment.

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The initial data extracted from the NTRI database encompassed the following variables: age, sex, the Glasgow Coma Scale (GCS), injury mechanism (falling, road traffic crash, and others), type of injury (isolated vs. The classification of patients into isolated and multiple trauma groups was based on whether the injury involved a single system or multiple systems, as determined by the Abbreviated Injury Scale (AIS). In-hospital mortality was defined as death occurring during the index hospitalization only; deaths that occurred after discharge were excluded. Subsequently, two reviewers-a physician and a medical student-obtained additional variables from the patients’ medical records via the Hospital Information System (HIS). These variables included the number of Packed Red Blood Cells (pRBCs) received, surgical interventions, the AIS, and the Injury Severity Score (ISS). Additionally, an anesthesiologist assessed and assigned the American Society of Anesthesiologists (ASA) score to all patients. Finally, using the extracted variables, trauma scoring systems-including GERtality, GTOS, GTOS II, Trauma Injury Severity Score (TRISS), and adjusted TRISS (aTRISS)-were calculated for each patient.

The AIS evaluates the severity of individual injuries across body regions, with scores ranging from 1 (mild injury) to 6 (fatal injury). The ISS is determined by summing the highest AIS scores from the three most severely injured body regions, with the total score ranging from 1 to 75, reflecting overall injury severity. To ensure consistency and reproducibility in variable abstraction and trauma score calculation, inter- and intra-observer agreement were assessed. For inter-rater reliability, two reviewers independently extracted data from 35 randomly selected patient records. Intra-rater agreement was evaluated by having each reviewer re-abstract data from 15 anonymized records after a 15-day interval. Intraclass Correlation Coefficients (ICC), Cohen’s kappa, and weighted kappa were used depending on the type of variable. ICC values were all above 0.86, and kappa values ranged from 0.88 to 0.92, indicating strong reliability. For the ASA score, which was assigned by an anesthesiologist, intra-rater agreement was assessed by re-evaluating 20 anonymized cases with a 20-day interval, yielding a weighted kappa of 0.89.

To calculate TRISS and aTRISS, the Revised Trauma Score (RTS) incorporates GCS, systolic blood pressure (SBP), and respiratory rate (RR), with each variable assigned a value ranging from 0 to 4. The RTS score ranges from 0 to 7.8408. For the age index, individuals under 55 receive a score of zero, while those aged 55 or older receive a score of one.

Quantitative variables were summarized using mean and standard deviation (SD) or median and interquartile range (IQR), as appropriate based on their distribution. Categorical variables were presented as frequencies and percentages. The student’s t-test or Mann-Whitney U test was used to compare continuous variables across outcome groups (in-hospital mortality and MV), depending on the normality of data. Discriminative performance of each scoring system was assessed using the concordance statistic (C-index), equivalent to the area under the receiver operating characteristic curve (AUC). To assess the risk of overfitting and to obtain optimism-corrected estimates, bootstrap resampling with 500 iterations was used. The calibration of each model was evaluated by comparing predicted probabilities with observed outcomes. This was quantified using the calibration intercept (assessing systematic under- or overestimation) and calibration slope (measuring agreement between predicted risks and actual outcomes). A calibration slope close to 1 and an intercept near 0 indicate good calibration.

To evaluate clinical usefulness, decision curve analysis (DCA) was conducted for each scoring system. DCA estimates the net benefit (NB) of using a model across a range of clinically relevant threshold probabilities. where w is the harm-to-benefit ratio, defined as the odds of the selected risk threshold (pt/(1 − pt)). This approach enables comparison of prediction models against default strategies such as treating all or no patients. To determine optimal cutoff points, a 5-fold cross-validation procedure was employed, consistent with TRIPOD recommendations for internal validation. The dataset was randomly divided into five equal subsets (folds). In each iteration, four folds (80%) were used to derive the optimal threshold based on Youden’s Index, while the remaining fold (20%) was used for validation. This process was repeated so that each fold served once as the validation set. The cross-validated C-index (cvC-index) and its standard deviation across folds were reported to assess both performance and stability. Finally, to examine the association between the derived cutoff values and outcomes, multiple logistic regression models were constructed, adjusting for relevant covariates including age, ISS, GCS, cause and type of trauma, and ASA classification. Variable selection was guided by the Hosmer-Lemeshow approach to ensure model parsimony and relevance.

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Patient Demographics and Characteristics

A total of 1,081 patients were included in the study. Of 1,081 GTPs, 586 (54.21%) were male. The mean (SD) age of the patients was 76.04 (7.8) years, with a significant difference between those who survived and those who did not (p < 0.001). The primary cause of trauma was falling (712 (65.9%)), followed by road traffic crashes (301 (27.8%)). Among all, 86 (7.9%) required MV, and 93 (8.6%) patients died during their hospitalization. The GERtality score had a median (IQR) of 2.0 (2.0) for deceased patients, compared to 0.0 (1.0) for survivors (p < 0.001). The GTOS showed higher values for deceased patients (median (IQR) = 110.5 (15.0)) vs. survivors (91.5 (19.0)) (p < 0.001).

Performance of Trauma Scoring Systems

The scoring system with the highest predictive performance for in-hospital mortality was GERtality, with a C-index of 0.89 (95% CI: 0.85 to 0.93). The other scoring systems with notable predictive performance were GTOS (C-index = 0.86, 95% CI: 0.83 to 0.94), GTOS II (C-index = 0.83, 95% CI: 0.78 to 0.88). The calibration plots of the scorings demonstrated that the GERtality score has the closest alignment with the line of identity, indicating its most accurate calibration for predicting in-hospital mortality [Calibration slop: 0.99, 95% CI: (0.80 to 1.17)].

The highest predictive performances for MV were observed with the GERtality and GTOS, both having C-index of 0.82, followed by TRISS (C-index = 0.80, 95% CI: 0.73 to 0.86), aTRISS (C-index = 0.80, 95% CI: 0.74 to 0.86).

The GERtality score yielded the highest NB across a wide range of threshold probabilities (approximately 0.05-0.85), underscoring its strong potential for guiding clinical decision-making related to MV requirements. It consistently outperformed the other scoring systems in terms of net benefit.

Logistic Regression Analysis

After adjusting for age, cause of trauma, ISS, GCS, and ASA, except for the GERtality model, where ASA was not included, the odds of in-hospital mortality in patients with a GERtality score ≥ 2 was 21.45 times higher than in patients with a score < 2 (Adjusted OR [aOR]: 21.45, 95% CI: 11.59 to 39.71, p < 0.001). Additionally, patients with a GTOS ≥ 100 exhibited significantly higher odds of in-hospital mortality (aOR: 15.66, 95% CI: 6.73 to 36.46, p < 0.001).

Conclusion

Prediction of unfavorable outcomes in GTPs is a critical challenge, as the global population constantly ages and the number of traumatic injuries grows in this vulnerable demographic. Iran is no exception to this trend, as well. Addressing these issues requires the development and application of reliable scoring systems to predict adverse outcomes such as mortality, improve clinical management, inform decision-making processes, and assist physicians in providing accurate prognostications for both patients and their families.

Several scoring systems have been proposed to address this challenge. Recent evidence highlights that predictive algorithms improve risk stratification and clinical decision-making by providing objective, data-driven assessments. In this study, the predictive value of several trauma scores, including GERtality, GTOS, GTOS II, TRISS, and aTRISS, for mortality and MV in a cohort of 1,081 GTPs was analyzed. Based on the literature, an AUC of 0.80 or higher is considered an acceptable diagnostic test, with an AUC of 0.90 or above regarded as excellent. In the current study, all trauma scores demonstrated acceptable predictive performance. Notably, GERtality, a specifically designed score for GTPs, stood out for its excellent predictive power for both in-hospital mortality and MV. Additionally, GTOS, GTOS II, TRISS, and aTRISS, which adjusts the TRISS for GTPs, also illustrated acceptable performance for predicting in-hospital mortality, with a descending predictive power. GERtality’s performance for predicting in-hospital mortality was slightly better than for MV, but GERtality, aTRISS, and TRISS all demonstrated good predictive accuracy for MV in GTPs. The superior performance of GERtality aligns with its prior validations in GTPs, such as the original development cohort by Scheerer et al. Findings reinforce this result, underscoring GERtality’s utility in predicting outcomes in geriatric trauma patients.

Galloway Township Public Schools

Galloway Township is a PK-6 public school district located in Atlantic County, NJ. Public school students in ninth through twelfth grades attend Absegami High School, located in the township. As of the 2021-22 school year, the high school had an enrollment of 1,169 students and 102.2 classroom teachers (on an FTE basis), for a student-teacher ratio of 11.4:1. Students in the western portion of the township have the option of attending Cedar Creek High School in neighboring Egg Harbor City under the school of choice program. Both high schools are part of the Greater Egg Harbor Regional High School District, a regional public high school district serving students from the constituent districts of Egg Harbor City, Galloway Township, Hamilton Township and Mullica Township. In the mid-1990s the student body increased as new employees of casinos moved into the area with their children. The district reached its peak enrollment, 3,975, in the 2002-2003 school year, which it maintained for the subsequent year. Due to the decline in the casino industry, the district began losing students. From 2006 to 2016 there were 60 district job cuts, with nine positions cut in 2016 alone. In 2010 the district proposed cutting 69 jobs. In 2010 the district had proposed closing both pre-kindergarten centers. Additionally between 2006 and 2010 it closed three schools.

The district had been classified by the New Jersey Department of Education as being in District Factor Group "CD", the sixth-highest of eight groupings. District Factor Groups organize districts statewide to allow comparison by common socioeconomic characteristics of the local districts. The student attendance boundaries changed in 2004 and it closed in 2005. The student attendance boundaries changed in 2004 and it closed in 2005. It opened due to an increase of students in the 1990s.

The district's board of education, comprised of nine members, sets policy and oversees the fiscal and educational operation of the district through its administration. As a Type II school district, the board's trustees are elected directly by voters to serve three-year terms of office on a staggered basis, with three seats up for election each year held (since 2012) as part of the November general election.

The School District is a Type II district located in the County of Galloway, State of New Jersey. As a Type II district, the School District functions independently through a Board of Education (the 'Board'). The Board is comprised of nine members elected to three-year terms. These terms are staggered so that three member's terms expire each year. The Superintendent is appointed by the Board to act as executive officer of the School District.

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