The DASH (Disabilities of the Arm, Shoulder and Hand) Outcome Measure: What do we know about it now?
Bibliographic record
Abstract
Outcome measurement is an essential component for defining the effectiveness of clinicians’ practice (Reiman 1988) and standardised measures make that job more consistent, comparable and valid (Cole et al 1994). Hand therapists have long recognised the need for the standardisation of outcome measures, particularly for performance-based measures such as strength testing (Mathiowetz et al 1985, Woody et al 1988) or joint motion. More recently there has been an increased interest in outcome measures that capture the patient's perspective of their status, and that are standardised. The DASH (Disabilities of the Arm, Shoulder and Hand) is a standardised outcome measure that could be used for this purpose (Hudak et al 1996, McConnell et al 1999). The DASH reflects the impact of a disorder in terms of physical function and symptoms, which are the two main reasons patients seek care for a disorder of the musculoskeletal system. The DASH is becoming widely used by clinicians and researchers (McConnell et al 1999). It is now important to revisit what we know about how well the DASH is able to measure what it purports to measure. The purpose of this paper is to review the research that has been done to date on the DASH outcome measure, and to describe the implications of this for future research and for clinical practice.
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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 | 0.039 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".