Factor Analysis and Item Reduction of the Banff Patella Instability Instrument (BPII)
Bibliographic record
Abstract
BACKGROUND: Clinical management of patellofemoral (PF) instability is a challenge, particularly considering the number of variables that should be taken into consideration for treatment. Quality of life is an important measure to consider with this patient population. PURPOSE: To factor analyze and reduce the total number of items in the Banff Patella Instability Instrument (BPII). Subsequent to the factor analysis, the new, item-reduced BPII 2.0 was tested for validity, reliability, and responsiveness. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: Quality of life was measured for PF instability patients (N = 223) through use of the original BPII at their initial consultation. Data from the BPII scores were used in a principal components analysis (PCA) to factor analyze and reduce the total number of items in the original BPII, to create a revised BPII 2.0. The BPII 2.0 underwent content validation (Cronbach alpha, patient interviews, and grade-level checking), construct validation (analysis of variance comparing the initial visit and the 6-, 12-, and 24-month postoperative visits, eta-square), convergent validation (Pearson r correlation to the original BPII), responsiveness testing (eta-square, anchor-based distribution testing), and reliability testing (intraclass correlation coefficient [ICC]). RESULTS: The BPII was successfully reduced from 32 to 23 items with excellent Cronbach alpha values in the new BPII 2.0: initial visit = 0.91; 6-month postoperative visit = 0.96; 12-month postoperative visit = 0.97; and 24-month postoperative visit = 0.76. Grade-level reading for all items was assessed as below grade 12. The BPII 2.0 was able to discriminate between all time periods with significant differences between groups (P < .05). Eta-square was 0.40, demonstrating a medium to large effect size. The BPII significantly correlated with the BPII 2.0 (0.82, 0.90, 0.90, and 0.94 at the initial visit and 6-, 12-, and 24-month postoperative visits, respectively), providing evidence of convergent validity. A significant correlation was found between the 7-point scale and 24-month postoperative BPII 2.0 scores, a sign of anchor-based responsiveness. ICC (2,k) was 0.97, indicating strong reliability. CONCLUSION: The BPII 2.0 is valid, reliable, and responsive for assessment of patients with PF instability, both surgically and nonsurgically treated.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".