Measures of Health-Related Quality of Life and Physical Function
Bibliographic record
Abstract
Outcome measures may be seen as windows, each of which provides a different perspective on a single view, the impact of a disease or disorder on the patient concerned. A comparison of the orthopaedic literature in 1991 with that in 2001 suggests that the intervening decade saw an increasing interest in the use of patient-based measures in clinical outcome studies, particularly randomized controlled trials. The tool most commonly used to determine the patient's point of view was the generic Short Form-36 (SF-36). Other measures included regional assessments such as the Musculoskeletal Functional Assessment, or the Disability of the Arm, Shoulder and Hand. The Western Ontario McMaster Osteoarthritis Index (WOMAC) (hip and knee), the Simple Shoulder Test (shoulder) and the Roland-Morris Questionnaire or Oswestry Disability Index (low back) were the most common joint-specific measures. Each of the questionnaires reported was supported by evidence of reliability and validity, and in most cases one or more studies had shown responsiveness in at least musculoskeletal disorders. We provide a brief description of the most common tools, and review the evidence that orthopaedic research is making increased use of measures of health status and function.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.006 | 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".