Absence of July effect on trauma outcomes at the start of the academic medical calendar
Bibliographic record
Abstract
Purpose The “July effect” refers to the phenomenon of a higher complication incidence and lower survival rate for patients treated at academic institutions during the beginning of the academic medical calendar. To date, no multi-center study has examined the presence of a “July effect” in a large cohort of trauma patients. We sought to determine if there is a by-month effect on survival and length of stay (LOS) for trauma patients in large academic centers. Methods We performed a multicenter, retrospective cohort study between 2010 and 2020 from five high-volume academic trauma centers in the United States. We included all adult motor vehicle collision (MVC) traumas evaluated at all centers in the study population (age >16 years, mechanism of injury classified as “motor vehicle collision”). Data included date and time of injury, injury severity score (ISS), LOS, and mortality. Each injury was classified as minor (ISS 1–8), moderate (9–15), severe (16–24), and very severe (>25). The mortality and LOS for each range over the entire epoch was calculated by month for each ISS classification. Results We analyzed 39,668 MVC traumas (mean age 41.83, range 39.9–43.3 among sites, 58.9% male and range 56.1–60.4%, 67.4% white range 42.3–84.7%). Survival for the “very severe” traumas ranged from 86.7–90.3%. Across the five sites, there was no significant change in survival (either increased or decreased mortality) or LOS for MVC patients in July or August for any injury classification level. Both age and ISS had a significant effect on survival (OR 0.02 for ISS >25, 0.22 for patients >65 years-old, both p < 0.05). Conclusions In five high-volume centers, there was no significant effect of the July transition on survival or LOS of MVC trauma patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".