Abstract 14369: “July Effect”: Impact of Academic Year Transition on In-Hospital Outcomes of Patients With Cardiac Arrest
Bibliographic record
Abstract
Introduction: The transition of training to a new Academic year and the beginning of training for new interns and fellows have been suggested to affect the quality of patient care and health care outcomes in critically ill patients, a phenomenon known as the “July Effect” in the United States. Prior studies have shown mixed reports regarding the “July Effect” existence. This study provides an insight into the “July Effect” in patients who suffered Cardiac arrest using a National Database. Methods: We performed a retrospective analysis using the National inpatient sample (2016- 2019). The NIS was searched for hospitalized adult patients with cardiac arrest as a principal diagnosis. We subsequently divided our sample into two cohorts based on the quarter of training (1st quarter, July to September, and the Last quarter of Training April to June). The primary outcome was inpatient mortality, and the secondary outcomes were the length of stay (LOS), total hospitalization charges (THC), and transfer frequency to other acute care facilities. Results: There were 27,940 Patients who suffered a cardiac arrest in the first and last quarter of our study period. Approximately 49.2% (13,755) vs. 50.8% (14,185) patients were identified in the 1st and Last quarters, respectively. There was a similar distribution of Age (63.5 ± 0.2) and Sex (Females) (43%) in both cohorts. There was similar inpatient mortality for patients who presented in both quarters (AOR 1.05, 95% CI 0.855-1.14, p=0.935). There was also no significant difference in LOS (adjusted coefficient -0.19, 95% CI -0.50-0.46, p=0.937), THC (adjusted coefficient 115.9, 95% CI -6483.46 - 6715, p=0.973) and frequency of transfer to other acute care facilities (AOR 0.95, 95% CI 0.82-1.11, p=0.57). Conclusions: This study suggests that the “July Effect” is not apparent in patients who suffered cardiac arrest as there were no significant differences in the rate of in-hospital mortality, length of stay, total hospitalization charges, and frequency of transfer to other acute care facilities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".