Bibliographic record
Abstract
Sir: Despite the assurance of a well-designed and rigorous meta-analysis showing no increased overall risk of perioperative surgical bleeding with ketorolac, we would echo and emphasize the authors’ caveat that one be selective in its use as determined by the type of procedure and patient risk. We recently published the experience at our center with the use of ketorolac in reduction mammaplasty, demonstrating a significant increase in surgical bleeding complications in patients treated with this agent.1 Admittedly, our study design lacked the precision of a randomized and blinded trial, but the data are compelling enough to arguably preclude one even being attempted. We feel that ketorolac should not be used in outpatient and short-stay surgical procedures where there is extensive surgical undermining in subcutaneous and prefascial tissue planes where large bleeding surfaces obscured by subcutaneous fat are created. Notwithstanding the obvious opioid-sparing benefit of ketorolac in these settings, we have stopped the routine use of this agent for reduction mammaplasty and abdominoplasty procedures. However, the authors have convincingly demonstrated a wide array of surgical settings in plastic surgery where ketorolac remains a useful therapeutic adjunct for postoperative analgesia. DISCLOSURE The authors have no financial interest in any of the products, devices, or drugs mentioned in this communication. John S. D. Davidson, M.D. Department of Surgery Division of Plastic Surgery Kim Turner, M.D. Departments of Anesthesiology and Perioperative Medicine Queen’s University Kingston, Ontario, Canada
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".