Perioperative Use of Gabapentinoids: Reply
Bibliographic record
Abstract
Correspondence| April 2021 Perioperative Use of Gabapentinoids: Reply Michael Verret, M.D., M.Sc. candidate, F.R.C.P.C.; Michael Verret, M.D., M.Sc. candidate, F.R.C.P.C. Search for other works by this author on: This Site PubMed Google Scholar Ryan Zarychanski, M.D., M.Sc., F.R.C.P.C.; Ryan Zarychanski, M.D., M.Sc., F.R.C.P.C. Search for other works by this author on: This Site PubMed Google Scholar François Lauzier, M.D., M.Sc., F.R.C.P.C.; François Lauzier, M.D., M.Sc., F.R.C.P.C. Search for other works by this author on: This Site PubMed Google Scholar Alexis F. Turgeon, M.D., M.Sc., F.R.C.P.C. Alexis F. Turgeon, M.D., M.Sc., F.R.C.P.C. Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information CHU de Québec - Université Laval Research Center and Université Laval, Québec City, Québec, Canada (A.F.T.). (Accepted for publication December 4, 2020. Published online first on January 6, 2021.) Anesthesiology April 2021, Vol. 134, 666–667. https://doi.org/10.1097/ALN.0000000000003667 Connected Content Commentary: Perioperative Use of Gabapentinoids: Comment Commentary: Perioperative Use of Gabapentinoids: Comment Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Search Site Citation Michael Verret, Ryan Zarychanski, François Lauzier, Alexis F. Turgeon; Perioperative Use of Gabapentinoids: Reply. Anesthesiology 2021; 134:666–667 doi: https://doi.org/10.1097/ALN.0000000000003667 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentAll PublicationsAnesthesiology Search Advanced Search We thank Dr. Araujo1 for his interest on our systematic review about the perioperative use of gabapentinoids for the management of postoperative acute pain.2 The main purpose of a systematic review with meta-analyses is to synthesize treatment effects considering all available data. By pooling data from different trials, the power to detect a differential effect is increased. Trials included in a systematic review frequently present some degree of clinical heterogeneity. However, not considering these trials in pooled analyses, as suggested by Dr. Araujo, would diminish our collective ability to both answer important research questions and understand the modifiers of important treatment effects. In our systematic review, we carefully considered potential sources of clinical heterogeneity among the included trials. First, we performed subgroup analyses to explore whether clinical heterogeneity or methodologic aspects of included trials could... Copyright © 2021, the American Society of Anesthesiologists, Inc. All Rights Reserved.2021 You do not currently have access to this content.
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 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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.030 | 0.040 |
| Insufficient payload (model declined to judge) | 0.012 | 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".