Bibliographic record
Abstract
Joint Osteoarthritis (OA) is a major source of morbidity and disability in the aging population. It is especially a concern to overweight individuals, as obesity is an important risk factor for the development of OA. The relationship between obesity and the development of joint damage is not purely the result of mechanical forces as pathologic inflammatory markers have been implicated in the process as well. Recent clinical practise guidelines state that weight-loss leads to functional improvement in joint pain. It can then be expected that bariatric surgery, the only proven long-term weight-loss modality in the morbidly obese, should have a similar effect. However, the improvement in OA post-surgery is not as drastic in comparison to the change in other obesity-related co-morbidities. Bariatric surgery, however, can be used in conjunction with orthopaedic surgery to ultimately treat OA through joint replacement. Effective weight-loss, as achieved through bariatric surgery, can improve post-operative outcomes in the obese population, making poor surgical candidates into acceptable ones. In this editorial, we explore the relationship between obesity and osteoarthritis and the roles of combining bariatric and orthopaedic surgeries.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".