Effect of anterior femoral cortical notch grade on postoperative function and complications during TKA surgery: A multicenter, retrospective study
Bibliographic record
Abstract
Abstract Purpose To explore the effect of AFN on knee function and complications in patients after TKA. Methods We evaluated 150 patients undergoing unilateral TKA, specifically including 102 patients with varying degrees of AFN after selection. They were divided into four groups based on AFN grade. About 48 patients did not produce AFN, 63 patients were grade I, 29 patients were grade II, and 10 patients were grade III. All patients were followed up for 24 months, and knee function, pain, complications, and other indicators were compared between the four groups. Correlation analysis and regression analysis were used to study the relationship between AFN and other indicators. Results Two cases of periprosthetic fractures (PPF) occurred in our study, with an incidence of 1.35%, which did not show a significant association with AFN. The changes in knee social score (ΔKSS), Western Ontario and McMaster Universities Osteoarthritis Index (ΔWOMAC), and postoperative anterior knee pain visual analog scale (VAS) score were higher in patients with AFN than in those without. Particularly, grades II and III AFN demonstrated superior efficacy. Pearson’s correlation analysis showed that AFN grade is positively correlated with both ΔKSS and ΔWOMAC (r = 0.44, P < 0.001), and AFN grade had a negative correlation with the anterior knee pain VAS (r = −0.250, P < 0.05). In linear regression analysis, AFN grade was positively correlated with both ΔKSS (β = 5.974, 95% CI: 3.968–7.981, P < 0.001) and ΔWOMAC (β = 6.356, 95% CI: 4.223–8.490, P < 0.001). Besides that, there was a negative correlation between AFN grade and anterior knee pain (β = 5.974, 95% CI: 3.968–7.981, P < 0.05). Conclusion Patients with grade II and III AFN who underwent TKA exhibited better knee function and lower levels of anterior knee pain post-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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".