Development of a patient-reported outcome: The Neck OutcOme Score (NOOS) – Content and construct validity
Bibliographic record
Abstract
OBJECTIVE: To develop a patient-reported outcome evaluating the impact of neck pain. The results of item generation and reduction and subscale structure in support of the content and construct validity of the measure are reported. METHODS: Items were generated from the literature and through focus groups including patients with neck pain and healthcare professionals, respectively. Item reduction was based on focus groups and field-tested questionnaire data. Construct validity was assessed using exploratory factor analysis. RESULTS: Focus groups containing 24 patients (mean age 57.2 (standard deviation (SD) 15.9) years, range 24-85 years); 19 women) and 12 healthcare professionals were conducted before data saturation was achieved. A total of 196 patients with neck pain (mean age 47.8 (SD 13.7) years), range 18-89 years; 146 women) completed the preliminary questionnaire. Overall 35 items were removed from the original 69. A multidimensional questionnaire, divided into five subscales, was developed from the remaining 34 items: mobility; symptoms; sleep disturbance; everyday activity and pain; and participation in everyday life. Exploratory factor analysis supported a 5-subscale structure. CONCLUSION: The Neck OutcOme Score has excellent content validity and preliminary results support a 5-subscale structure. Additional work is needed to assess the reliability, further construct validity and responsiveness.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".