Time of day and 30‐day mortality after emergency surgery. A reply
Bibliographic record
Abstract
We thank Dr Kamal for his interest in our study 1. We included the following variables in our logistic regression model: age; sex; ASA physical status; emergency category; day of surgery; duration of anaesthesia; and type of surgery. In the paper, we acknowledged that other pre-existing patient morbidities may have varied with the time of surgery and might have affected our results. We acknowledged that our inability to determine the duration of time that the patients waited for surgery was a limitation of our study and agree that this has been shown to impact postoperative mortality 2. We were concerned that surgical or anaesthetic sleep deprivation might have a negative impact on patient care. However, we also considered that fewer hospital personnel might be available overnight, or less familiar with the equipment needed relative to the staffing during the regular working day, also with negative consequences. Our results did not reach statistical significance (p < 0.05), but we consider postoperative mortality, although readily quantifiable, to be an extreme end-point. Further investigation is warranted to determine if there are increased morbidities caused by operating overnight relative to daytime for emergency procedures, hence the conclusion in our paper.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".