Bibliographic record
Abstract
Sir: We would like to thank Drs. Davidson and Turner for their comments regarding our article entitled “Ketorolac Does Not Increase Postoperative Bleeding: A Meta-Analysis of Randomized Controlled Trials.” It appears that they have published their data in the Canadian Journal of Anaesthesiology.1 Our article focused on a review of prospective, double-blind, randomized studies. As a retrospective study, although provocative, their study was not of a high enough level of evidence for us to include in our review. They do bring up the important point of understanding the risk-to-benefit issues when considering the use of drugs in the perioperative period. The question of performing elective operations on patients with either antiplatelet or anticoagulant therapies will likely become more of an issue in the future. Certainly, our report does not definitely answer the question about whether using ketorolac in breast reduction is appropriate. It simply shows where the available evidence is today and highlights where we have gaps in our knowledge. For breast reduction surgery, surgeons will have to use the available published evidence combined with their own experience and evaluation of individual patients to make this decision. Carefully performed prospective, blinded, randomized studies will ultimately need to be performed to provide more definitive guidance. DISCLOSURE The authors have no financial interest in any of the products, devices, or drugs mentioned in this communication. Ryan M. Gobble, M.D. Dennis P. Orgill, M.D., Ph.D. Brigham and Women’s Hospital Boston, Mass. [email protected]
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 | 0.005 | 0.061 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.014 | 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".