July Effect in Obstetric Outcomes
Bibliographic record
Abstract
OBJECTIVE: The July effect represents the month when interns begin residency and residents advance with increased responsibility. This has not been well studied in Obstetrics and Gynecology residencies and no study has been conducted evaluating obstetric outcomes. The purpose of this study was to evaluate the July effect on obstetric outcomes. Women who delivered between July and September (quarter 1) were compared to those delivering between April and June (quarter 4). METHODS: This retrospective cohort study compared outcomes of deliveries between quarter 1 and quarter 4 from 2017 to 2020. Outcomes evaluated were postpartum length of stay (LOS), postpartum readmission, wound complication, wound infection, blood transfusion, estimated blood loss, 3rd and 4th degree lacerations, 5 min APGAR scores, and cesarean delivery rates. RESULTS: There were 3693 deliveries in quarter 1 and 3107 deliveries in quarter 4. There was a higher incidence Of wound infection during the April-June period (N = 21; 0.68%) compared to July-September (N = 10; 0.27%; p = 0.0135). Although LOS for both periods were the same, the average postpartum LOS during July-September was slightly longer than April-June (1.7 days; SD = 1.1 vs 1.6 days; SD = 1.2; p = 0.0026). All other pregnancy outcomes were similar between the two groups. CONCLUSION: Overall, the July effect is minimal on obstetric complications. However, LOS between July and September may differ because all residents are less experienced in quarter 1. Wound infection rates were higher in April-June, perhaps because new PGY-1s went from assisting to primary on cesarean surgeries starting in the 4th quarter of the year.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".