The validity and reliability of the Turkish version of the Patient’s Knee Implant Performance (PKIP) questionnaire-preoperative and postoperative
Bibliographic record
Abstract
The Patient's Knee Implant Performance (PKIP) Questionnaire is a short and easy-to-complete questionnaire developed to assess the performance of total knee arthroplasty (TKA) more comprehensively. The aim of this study was to investigate the validity and reliability of the Turkish version of the PIKP questionnaire before (PKIP PreOp) and after (PKIP PostOp) TKA. The study included 162 patients referred for TKA and 154 patients who had undergone the surgery at least 3 months prior. Cronbach alpha, intra-class correlation coefficient, and item-total correlation values were calculated to assess the reliability of the PIKP questionnaire. Validity was determined using exploratory and confirmatory factor analysis. To determine parallel scale validity, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), The Short Form-36 (SF-36) Quality of Life Scale performance score were used. The mean age of the participants was 66.39 ± 7.65 years. Cronbach alpha and intra-class correlation coefficient values of the PKIP were acceptable (0.723 and 0.985, respectively). The item-total correlation values of each item of the PKIP was also acceptable (lowest ranged from 0.335 to 0.621). Confirmatory and exploratory factor analysis revealed that the both PKIP PreOp and PKIP PostOp studies had sufficient fit. The PKIP PreOp and PKIP PostOp was moderately to strongly correlated with the Western Ontario and McMaster Universities Osteoarthritis Index and Short Form-36 score (P < .001). Patients undergoing TKA had a significantly higher PKIP PostOp score than PKIP PreOp score. The Turkish version of the PKIP is valid, reliable, and sensitive to assess in performance in patients undergoing TKA.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".