Ketorolac May Increase Hematoma Risk in Reduction Mammaplasty: A Case-control Study
Bibliographic record
Abstract
Background: Ketorolac is a potent nonsteroidal anti-inflammatory drug that has valuable analgesic properties but also a hypothetical risk of increased bleeding due to inhibition of platelet activation. The clinical significance of this risk, however, is unclear when it is used after reduction mammaplasty. Our study objective was to therefore examine the association between ketorolac exposure and hematoma occurrence after breast reduction surgery. We hypothesized that there was no association between ketorolac exposure and hematoma occurrence in breast reduction surgery. Methods: A case-control design was used. Data from charts of all reduction mammaplasties that developed hematomas requiring surgical evacuation (cases) at our university-based hospitals were retrieved and matched to data from charts of reduction mammaplasty patients who did not indicate this complication (controls). Matching occurred in a 1:1 ratio based on 4 criteria: age, body mass index, institution, and preexisting hypertension. Charts were reviewed for retrospective information on exposure to ketorolac. Odds ratio (OR) was calculated with an OR > 1 favoring an association. Results: From 2002 to 2016, 40 cases of hematoma met inclusion criteria and were matched with 40 controls (N = 80). Cases had a significantly lower body mass index than controls; however, the other baseline patient demographics were similar between the 2 groups. There was an association between hematoma formation and exposure to ketorolac (OR, 2.4; 95% confidence interval, 0.8–7.4; P = 0.114) and a trend for greater risk of hematoma formation, although this was not statistically significant. Conclusions: Based on this level 3 evidence, there appears to be an association between perioperative ketorolac exposure and hematoma after breast reduction surgery, but it was not statistically significant. Although this study was adequately powered, the OR of 2.4 was associated with a wide confidence interval. A larger sample size may increase the precision of the results and may also make the association definitive.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".