Bibliographic record
Abstract
Obesity is a growing worldwide health issue! In my home country, the percentage of obese Canadians grew from 13.8% in 1979 to 23.1% in 2004. Interestingly, TKA rates have grown substantially during this time frame and obesity seems to have been a major contributor. In a large study, we found that increasing obesity had an exponential effect on TKA rates (i.e. patients with a body mass index >40 having a 33X greater relative risk of receiving a TKA compared to a normal weight patients). This is an important issue, as obese TKA patients have been shown to have greater pre-operative disability, have longer waits for surgery, be associated with greater technical difficulties (i.e. wound healing, infection, ligamentous injury, deep vein thrombosis and medical issues) and have more peri-operative complications. As a result, some countries have advocated deferring TKAs in obese patients until they have lost a substantial amount of weight despite the fact that many studies have demonstrated that the required weight reduction is seldom achieved. In an effort to understand this issue, we have conducted several studies. In a multicentre study, we could find no link between patient obesity and the level of patient satisfaction following a primary TKA. In another mid-term study, we found that obese patients had equal implant survivorship, but did note that obese patients had lower pre-operative and post-operative health-related quality of life outcome scores. However, in this manuscript we advocated determining the ‘improvement or delta score’ (i.e. difference between the pre-operative and post-operative scores) and found that when this was done, obese TKA patients actually demonstrated more improvement than normal and overweight patients! Based on our research, we would make the following recommendations: (1) the public should be educated on the effect of obesity on TKA rates, (2) weight management should be an important part of non-operative knee arthritis management and (3) TKA should ‘not’ be withheld from obese patients with end-stage knee arthritis.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".