A commentary on “Timing of surgery for aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis”
Bibliographic record
Abstract
We are interested in the article entitled ʻTiming of surgery for aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysisʼ, which was published in the International Journal of Surgery [1] .The meta-analysis included one randomized controlled trial (RCT) and 13 non-RCTs.Except for the pooled analysis of poor outcomes and death, the meta-analysis conducted a subgroup analysis on differences in the study type, country, publication year, age, condition before surgery, and follow-up to identify sources of heterogeneity.The authors concluded that early surgery was superior to late surgery in reducing poor outcomes and death rates when patients were in good condition on admission and decreased the incidence of poor outcomes when patients were in poor condition on admission.The subgroup analyses were done well, although there were several questions in the study that we need to consider.First, most of the included studies were non-RCTs, and there was only one RCT.The use of the Newcastle-Ottawa Scale (NOS) by the authors to evaluate the literature quality was appropriate.While there was a significant difference in the NOS scores among the 14 studies, a subgroup analysis on NOS scores was not performed in the meta-analysis.Second, there was statistical heterogeneity in the subgroup analyses stratified by the study type, country, year of publication, age, clinical condition before surgery, clinical condition on admission and follow-up time.It was difficult to confirm the source of heterogeneity in their subgroup analyses [2] .Third, it seems that the sample sizes of the studies included in the subgroup analyses for poor condition on admission were small, and the authors did not include this important index in the table of the characteristics of the included studies.In the forest None.
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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 | 0.031 | 0.173 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.035 | 0.032 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".