Indication for total knee arthroplasty based on preoperative functional score: Are we operating earlier?
Bibliographic record
Abstract
Background: The number of total knee arthroplasty (TKA) procedures performed annually is increasing for reasons not fully explained by population growth and increasing rates of obesity. The purpose of this study was to determine the role of patient functional status as an indication for surgery and to determine if patients are undergoing surgery with a higher level of preoperative function than in the past. Methods: A systematic review and meta-analysis of the MEDLINE, Embase and Cochrane databases was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Functional status was assessed using the 36-Item Short Form Health Survey’s physical component summary (PCS) score. Only primary procedures were included. Articles were screened by 2 independent reviewers, with conflicts resolved with a third reviewer. Meta-regression analysis was performed to determine the effect of time, age and sex on preoperative PCS score. Subgroup analysis was performed to compare results for the United States with those for the rest of the world. Results: A total of 1502 articles were identified, of which 149 were included in the study. Data from 257 independent groups including 57 844 patients recruited from 1991 to 2015 were analyzed. The mean preoperative PCS score was 31.1 (95% confidence interval 30.6–31.7) with a 95% prediction interval of 22.8–39.5. The variance across studies was found to be significant (p < 0.001) with 99.01% true variance. Year of enrolment, age, the percentage of female patients and geographic region did not have any significant effect on preoperative PCS score. Conclusion: Patients are undergoing TKA with a level of preoperative function similar to their level of function in the past. Patient age, sex and location did not influence the functional status at which patients were considered to be candidates for surgery.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".