Author response to: Comment on: Timing of symptomatic venous thromboembolism after surgery: meta-analysis
Bibliographic record
Abstract
Dear Editor We appreciate Dr Yang for his comments on our paper, which provide us with an opportunity to clarify the methods of our study. First, because retrospective studies often miss post-discharge venous thromboembolism (VTE) events, we included only prospective studies1. As surgical and perioperative practices have vastly changed over time, we included only studies with patient recruitment in the year 2000 or after. To mitigate the effect of publication bias, we included studies that had at least 20 postoperative VTE events. We included studies conducted in various surgical fields. Because we believed that timing of postoperative VTE events might not be generalizable from obstetrics, pediatric, cardiac, and neurosurgery to other surgical fields, we excluded these surgical subspecialties. Second, in his editorial comment Dr Yang called for reporting measures of heterogeneity. Heterogenity in the outcome, incidence of VTE at each day since surgery, was explained by days since surgery (a spline), studies and their interaction using a Poisson regression. All effects were considered fixed. No natural heterogenity among studies was considered, in the Poisson regression the hypothesized rate of VTE was assumed to be constant over the study period for different studies. If authors had not reported number of events and/or population sizes, we extracted data from original papers by digitizing figures. Finally, as studies included in our study had very consistent results, sensitivity analyses by excluding one study at a time would not have changed results.
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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 | 0.021 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".