Commentary on: Postoperative Antibiotics Following Reduction Mammaplasty Does Not Reduce Rates of Surgical Site Infection
Bibliographic record
Abstract
See the Original Article here. There is still confusion around the recommendations for whether or not to give postoperative antibiotics to breast reduction patients, especially when they are obese or have high resection weights. The authors have tried to solve this question, as have many before them. The authors utilized information derived from a private commercial database called PearlDiver (Colorado Springs, CO).1 This company uses private insurance claims from various American providers such as Humana (Louisville, KY) and United Healthcare (Minnetonka, MN), as well as government claims from Medicare. These data appear to be quite comprehensive, but their website is opaque, and I have not yet received an answer to my questions from them. The populations are not randomly sampled but that may not make a difference for this study. The authors state that the advantage of this private insurance–derived database is that it follows patients for 90 days instead of the 30 days employed in the National Surgical Quality Improvement Program (NSQIP) database. This may not be an important point, because most infections appear within the 30-day window. Aside from its limitations, the NSQIP database is widely believed to be the most precise and accurate database available for measuring patient outcomes after surgery, given its high rate of complete data sets, operative data points, validation, and interrater reliability. It is clear, however, that any database today has its limitations.
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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.055 | 0.034 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".