The pre-anesthetic period is the best time to evaluate the knee flexion angle for predicting the flexion angle after total knee arthroplasty: A prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Knee flexion angle (KFA) is one of the most critical factors for evaluating patient functional outcomes after total knee arthroplasty (TKA). Preoperative KFA and intraoperative drop leg test are both accepted as predictors of postoperative KFA after TKA. Preoperative testing performed after anesthesia helps overcome pain-related limitations; however, the KFA measurement timepoint that best predicts KFA at 6 months after TKA has not yet been established. METHODS: This prospective cohort study recruited patients who underwent unilateral primary TKA at Siriraj Hospital (Bangkok, Thailand) during August 2012 to August 2017. We recorded KFA at the pre-anesthetic phase, post-anesthetic phase, intraoperation using drop leg test, and at 6-months post-operation. Pearson's correlation coefficient was used to evaluate correlation between different measurement timepoints and 6 months after surgery. Those same relationships were evaluated for overall patients, and for patients with KFA <90° (poor KFA), 90-120° (average KFA), and >120° (high KFA). RESULTS: A total of 165 patients with a mean age of 68.7 years were recruited. Pre-anesthetic KFA measurement had the highest positive correlation with the 6-month KFA (r = 0.771, p<0.05). Post-anesthetic measurement and intraoperative drop leg KFA measurement had moderate positive correlation (r = 0.561, p<0.05) and low positive correlation (r = 0.368, p<0.05) with the 6-month KFA, respectively. The average KFA group had the highest positive correlation between pre-anesthetic KFA measurement and the 6-month KFA (r = 0.711, p<0.05). Predicted 6-month KFA (degrees) adjusted for pre-anesthetic KFA is 45.378 + [0.596 x pre-anesthetic KFA (degrees)] (r = 0.67, p <0.05). CONCLUSIONS: Pre-anesthetic KFA demonstrated the highest correlation with the final KFA at six months after unilateral primary TKA, especially in the patients who had a preoperative KFA within 90-120°.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".