Comment on: “Buprenorphine for acute post-surgical pain: A systematic review and meta-analysis”
Bibliographic record
Abstract
Dear Editor, I read with great interest the recently published systematic review and meta-analysis (SRMA) on the efficacy of buprenorphine for acute post-surgical pain.[1] I congratulate Albaqami et al.[1] for this great study and wish to present my insights on that article. Albaqami et al.[1] state in the “Discussion” that “this was the first SRMA that evaluate the efficacy of transdermal buprenorphine and sublingual buprenorphine for postoperative pain management”. Although it might be technically correct, I wish to point out that another SRMA on this topic[2] got published recently. The only and slight difference is that while Albaqami et al.[1] included studies using both transdermal and sublingual buprenorphine, Machado et al.[2] included only transdermal buprenorphine studies. Even if we consider that this SRMA is unique in that it included both these routes when compared to the previously published SRMA,[2] Albaqami et al.[1] didn’t elaborate on the sublingual buprenorphine studies (reference # 11,21, 22,24 of Albaqami et al.[1]) anywhere in the “Discussion”. Moreover, the Vancouver style of referencing was violated as these 15 studies (references #11 to 25) were not cited before citing references 26, and 27 in the “Discussion”. Albaqami et al.[1] state in the “Methodology” that “Studies using fentanyl and tramadol in the control group were considered eligible as fentanyl and tramadol are well studied in clinical trials and understood opioids”. However, studies using a placebo and other drugs such as parecoxib, celecoxib, lurbiprofen, etc., for comparison were also included. Last but not least, Albaqami et al.[1] didn’t include a few eligible studies for this SRMA. For instance, studies by Nanda et al.[3] and Li et al.,[4] published in 2020 should have been included. Although another study by Patanwala et al.[5] got published in March 2022, I feel that this study could have been included in this SRMA. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.028 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.032 | 0.032 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".