Content validation of the Patient-Reported Hamilton Inventory for Complex Regional Pain Syndrome
Bibliographic record
Abstract
BACKGROUND: Complex regional pain syndrome (CRPS) is a perplexing neurological condition, and persons with CRPS experience substantial loss of daily roles and activities. A condition-specific measure is being developed to evaluate CRPS. PURPOSE: We describe the use of cognitive interviews to examine content validity of this patient-reported outcome measure for CRPS. METHOD: Interviews with 44 persons with CRPS were analyzed to identify problems with wording and support content validation. Item-total correlations were calculated for proposed subscales, and scores were plotted to consider floor/ceiling effects. FINDINGS: Interviews identified questions where respondents considered factors unrelated to the construct of interest or were underaddressed by the questionnaire, including depression and skin temperature. The symptoms, daily function, and coping/social impact scales demonstrated satisfactory correlations (Cronbach's alpha 0.76-0.86). Despite a sampling bias of severity, no frank floor/ceiling effects were noted. IMPLICATIONS: This study builds a foundation for continuing development and evaluation of the measurement properties of the Patient-Reported Hamilton Inventory for CRPS. It makes explicit the iterative decisions involved in rigorous instrument development.
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 | 0.033 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".