Local Administration of Morphine for Analgesia after Iliac Bone Graft Harvest
Bibliographic record
Abstract
Department of Anesthesia, Toronto Western Hospital, Toronto, Ontario, Canada. rjsawyer@hotmail.comTo the Editor:—I was very interested to read the article “Local Administration of Morphine for Analgesia after Iliac Bone Graft Harvest” and would like to commend the authors on a good article. 1I would, however, like to mention two points that may have adversely influenced the resultsof the study discussed.The patients were scheduled for elective decompressive cervical laminectomy; however, it is not recorded whether these patients were on opioids or nonsteroidal anti-inflammatory drugs (NSAIDs) for preoperative pain management. Chronic persistent cervicogenic pain can be a preoperative presenting complaint and preoperative analgesic usage can influence postoperative analgesic requirements. 2The authors also do not adequately describe the method of harvest site injection. Was contact with the donor site bone made or was this merely a local infiltration into the surrounding tissues? In trying to reproduce the local injection technique we were unable to avoid local tissue injection. This unfortunately would result in a third space tissue depot injection of 5 mg of morphine with slower systemic absorption, and hence a more prolonged effect. This morphine effect would be present during the first 24 h of the study period. Unfortunately, the authors did not report pain scores beyond the first 24 h.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".