Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to develop a short, reliable, and valid measure of physical function and symptoms related to upper-limb musculoskeletal disorders by shortening the full, thirty-item DASH (Disabilities of the Arm, Shoulder and Hand) Outcome Measure. METHODS: Three item-reduction techniques were used on the cross-sectional field-testing data derived from a study of 407 patients with various upper-limb conditions. These techniques were the concept-retention method, the equidiscriminative item-total correlation, and the item response theory (Rasch modeling). Three eleven-item scales were created. Data from a longitudinal cohort study in which the DASH questionnaire was administered to 200 patients with shoulder and wrist/hand disorders were then used to assess the reliability (Cronbach alpha and test-retest reliability) and validity (cross-sectional and longitudinal construct) of the three scales. Results were compared with those derived with the full DASH. RESULTS: The three versions were comparable with regard to their measurement properties. All had a Cronbach alpha of > or = 0.92 and an intraclass correlation coefficient of > or = 0.94. Evidence of construct validity was established (r > or = 0.64 with single-item indices of pain and function). The concept-retention method, the most subjective of the approaches to item reduction, ranked highest in terms of its similarity to the original DASH. CONCLUSIONS: The concept-retention version is named the QuickDASH. It contains eleven items and is similar with regard to scores and properties to the full DASH. A comparison of item-reduction approaches suggested that the retention of clinically sensible and important content produced a comparable, if not slightly better, instrument than did more statistically driven approaches.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".