Re: Role of Obesity on the Risk for Total Hip or Knee Arthroplasty
Bibliographic record
Abstract
To the Editor: We read with great interest the article “Role of Obesity on the Risk for Total Hip or Knee Arthroplasty” by Bourne et al. [1] in the December 2007 issue of CORR. The authors state increasing obesity was associated with increased relative risk for hip or knee arthroplasty. They found almost 75% of total hip arthroplasty recipients and 88% of total knee arthroplasty recipients were overweight or obese whereas the percentage of overweight or obese people for the Canadian population is 51.4%. These percentages could reflect a relationship between obesity and the risk of hip and knee arthroplasties, but there are no data regarding the etiologic factors leading to obesity in these patients. Arthrosis of the hip or the knee causes pain in the affected joint and typically results in a decline in physical activity; a sedentary lifestyle and physical inactivity lead to obesity [2]. It is unclear whether obesity causes arthrosis or arthrosis leads to physical inactivity and thus to obesity. If the latter is correct, one also would anticipate a higher percentage of obese patients in a population undergoing arthroplasty. Patients complaining of being overweight because of inactivity imposed by their pain is not uncommon. We believe this requires additional research for a definitive answer.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".