Bibliographic record
Abstract
It was a pleasure to read the article from Verret et al. in a recent issue of Anesthesiology.1 The main prerequisites of a high-quality systematic review were met; however, some considerations about the patient, intervention, comparison, and study design approach applied and the conclusion of the review must be addressed. The main outcomes of the meta-analysis pooled results of highly divergent procedures, from endoscopic procedures to major surgeries. A relevant issue in both the Enhanced Recovery after Surgery and the Perioperative Surgical Home doctrines is the specificity of surgical route standardization.2,3 The possibility of another analgesic regiment being used in the comparator group is another concern. The use of gabapentin was compared with antidepressants, opioids, ketamine, nonsteroidal anti-inflammatory drugs, corticosteroids, benzodiazepines, neuroleptics, anticonvulsant, α2-adrenergic receptor agonists, paracetamol, melatonin, and placebo. The use of each of these agents incites completely different conclusions, and—with the exception of the placebo—would not give any idea about the effectiveness of the studied intervention when pooled.Beyond the clinical heterogeneity, the statistical heterogeneity is also a major issue. The inconsistency (I2) over 75% (considerable heterogeneity according the Cochrane Handbook for Systematic Reviews of Interventions) found in every acute and subacute pain summary measure of the review confirms the previous considerations.4 Its high level of statistical heterogeneity is considered a prohibitive feature when considering performing a meta-analysis in many systematic review protocols and raises many concerns about the conclusions of the review announced by the authors. That statistical heterogeneity must dictate how the results are understood and reflects the aforementioned clinical heterogeneity.The inclusion of unequal study populations and comparison groups increased the precision of the estimates, while it also reduced the applicability of the results. The low certainty of evidence rated for the primary outcomes implies that the true effect is probably markedly different from the estimated effect.5 The assumption that further research is unlikely to change the conclusion about the effectiveness of gabapentinoids in early postoperative analgesia is consistent with the trial’s sequential analysis, but does not contemplate the clinical and statistical heterogeneity between the included studies.The author declares no competing interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".