Letter to the Editor Comment on ‘‘The Effects of Bariatric Surgery Weight Loss on Knee Pain in Patients with Osteoarthritis of the Knee’’
Bibliographic record
Abstract
Copyright © 2013 Janice Lin et al.This is an open access article distributed under theCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It was with great interest that we read “The Effects of Bariatric Surgery Weight Loss on Knee Pain in Patients with Osteoarthritis of the Knee ” by Edwards et al. [1]. As other studies have shown, obesity is an incrementally modifiable risk factor for the development and progression of knee osteoarthritis (KOA) [2, 3]. Any opportunity to treat obesity and potentially limit KOA progression and disability will be an important public health strategy. Bariatric surgery is superior to regimented dietary and exercise programs in helping obese patients achieve andmaintainweight loss [4, 5], and thus we think the authors focused on an important potential option to help obese patients avoid total joint replacement surgery. We found it valuable that the authors conducted a joint-driven and hypothesis-driven study to track pain and functional improvement in patients who underwent the three types of bariatric surgery: gastric bypass, sleeve gastrectomy, and laparoscopic adjustable gastric banding (LAGB). We agree with the authors that the existing literature is limited in examining the effect of bariatric surgery onKOA, particularly with confirmation of patients ’ radiographic KOA and the utilization of validated tools such as Western Ontario and
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.024 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".