Is there any evidence of a “July effect” in patients undergoing major cancer surgery?
Bibliographic record
Abstract
BACKGROUND: The "July effect" refers to the phenomenon of adverse impacts on patient care arising from the changeover in medical staff that takes place during this month at academic medical centres in North America. There has been some evidence supporting the presence of the July effect, including data from surgical specialties. Uniformity of care, regardless of time of year, is required for patients undergoing major cancer surgery. We therefore sought to perform a population-level assessment for the presence of a July effect in this field. METHODS: We used the Nationwide Inpatient Sample to abstract data on patients undergoing 1 of 8 major cancer surgeries at academic medical centres between Jan. 1, 1999, and Dec. 30, 2009. The primary outcomes examined were postoperative complications and in-hospital mortality. Univariate analyses and subsequently multivariate analyses, controlling for patient and hospital characteristics, were performed to identify whether the time of surgery was an independent predictor of outcome after major cancer surgery. RESULTS: On univariate analysis, the overall postoperative complication rate, as well as genitourinary and hematologic complications specifically, was higher in July than the rest of the year. However, on multivariate analysis, only hematologic complications were significantly higher in July, with no difference in overall postoperative complication rate or in-hospital mortality for all 8 surgeries considered separately or together. CONCLUSION: On the whole, the data confirm an absence of a July effect in patients undergoing major cancer surgery.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".