Response to Letter Regarding Article, “Drug-Eluting or Bare Metal Stents for the Treatment of Saphenous Vein Graft Disease: A Bayesian Meta-Analysis”
Bibliographic record
Abstract
As pointed out by Hakeem et al, other meta-analyses have been published recently on the use of drug-eluting versus bare metal stents in saphenous vein grafts. Because the data on which they are founded are available to researchers worldwide, meta-analyses are certainly more prone to redundancy than original research. However, we still believe that our meta-analysis differed slightly from other reviews because it was the first to use a bayesian hierarchical random-effects model. Moreover, at the time of our literature search, the number of patients collected in our meta-analysis was the highest ever reported. Finally, our work included new data from contemporary studies, including, for example, the STENT registry. e agree with Hakeem et al on the fact that there is a need for a facility that will assume a prospective registration of protocols for systematic reviews and meta-analyses of studies evaluating health care interventions. This kind of centralized open registry would consequently provide more transparency throughout the publication process, from protocol redaction to revelation of potential bias. This would also avoid superfluous duplication. In fact, a simple, free, web-based, easily searchable platform would be beneficial to inform medical researcher on ongoing reviews and meta-analyses. Thus, we support initiatives like the PRISMA statement, 2 which will probably lead to a more efficient use of the finite research funding and to an improvement of the overall quality of systematic reviews and meta-analyses.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".