Patient participation in patient-reported outcome instrument development in systemic sclerosis.
Bibliographic record
Abstract
OBJECTIVES: The patient perspective captured using Patient-Reported Outcome (PRO) instruments provide insight into the patient condition not always captured by physician-derived assessment tools. Target patient population involvement is an essential component of PRO instrument development. We have reviewed the level of patient involvement in the development of PRO instruments used in the assessment of systemic sclerosis (SSc). METHODS: A comprehensive literature review was undertaken to identify studies reporting PRO instruments in SSc. Studies were assessed to establish whether the PRO instruments had been developed specifically for SSc or adopted from other disease areas. Studies reporting PRO instruments specific for SSc were scrutinised for evidence of target patient population involvement in the development of the instrument. RESULTS: A total of 58 PRO instruments that have been used in SSc research were identified. Twelve (21%) of these were developed specifically for outcome assessment within SSc populations. Of these, 5 (42%) had not reported any patient involvement in the development phase of the instrument. Five SSc PRO instruments (42%) involved target patient population in the domain/item generation stage. Four (33%) of SSc PRO instruments had undertaken cognitive interviewing to ensure item wording adequately captured the intended conceptual framework. CONCLUSIONS: The majority of PRO instruments used to assess SSc have not involved significant target patient involvement in their development. By involving patients in the development of novel PRO instruments in SSc, we can ensure such instruments adequately capture the experiences most relevant to our patients.
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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 | 0.118 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".