Development and Evaluation of an Activity Rating Scale for Disorders of the Knee
Bibliographic record
Abstract
Reports of clinical studies of patients with knee disorders should routinely include their activity levels to enable comparison of treatment groups and to allow generalizability. The goal of this study was to develop and evaluate a new rating scale to measure activity levels of patients. We assessed reliability by administering the scale to 40 subjects on 2 separate occasions, 1 week apart. Validity was evaluated by comparing the activity rating on the new scale with that from other instruments that use activity level scales (concurrent construct validity) and also by correlating the score on the new scale with age (divergent validity). Patients easily understood the scale and were able to complete it in 1 minute. The reliability was high (intraclass correlation coefficient, 0.97). The scale also correlated well with existing activity rating scales: Spearman correlation coefficient for Cincinnati score, 0.67; for Tegner scale, 0.66; for Daniel scale, 0.52. The activity score was significantly inversely correlated with age (P = 0.002), indicating divergent validity. This instrument will facilitate generalizability of results and allow more accurate comparisons among patient groups in outcomes research in sports medicine.
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 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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".