Introduction of a classification system for patients with patellofemoral instability (WARPS and STAID)
Bibliographic record
Abstract
PURPOSE: The primary purpose of this paper is to introduce the WARPS/STAID classification system for patellofemoral instability. The secondary purpose is to establish the validity and reliability of the WARPS/STAID classification system. METHODS: Patients (n = 31) with a confirmed diagnosis of patellofemoral instability underwent a thorough knee history and physical examination with 3 raters. The raters graded each component of the WARPS/STAID classification system on a visual analogue scale (VAS). A single Global VAS WARPS/STAID score was graded after all other components of the classification system were completed. Intraclass correlation coefficient (ICC 2, 3) was calculated for each metric of the classification scale and for the Global score. Concurrent validity was assessed by correlating the WARPS/STAID score with the Kujala score. Subjects were assigned to one of three categories (WARPS, STAID, or mixed characteristics) according to the Total WARPS/STAID score to determine the level of agreement between the three raters. RESULTS: Intraclass correlation coefficient (ICC 2, 3) of the WARPS/STAID classification continuum ranged between 0.73 and 0.91 for the individual metrics of the classification. The ICC (2, 3) for the Global WARPS/STAID score was 0.75. The mean Kujala score (m = 61, SD 18) was significantly correlated with the total WARPS/STAID score (r = 0.387, p < 0.05). The majority of subjects were assigned to either the WARPS or STAID categories. CONCLUSION: This study introduced the WARPS/STAID classification system and established both validity and reliability in subjects with patellofemoral instability. LEVEL OF EVIDENCE: II.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".