Modification of the SF‐36 for a Headache Population Changes Patient‐Reported Health Status
Bibliographic record
Abstract
BACKGROUND: Using standard quality of life and disability measures may not accurately capture these constructs in specific health populations such as headache patients. Modifying the wording of standard measures such as the Short-Form 36 (SF-36) should be considered in order to make them more applicable to specific patient populations. OBJECTIVE: To investigate the possibility that headache patients may not consider their headaches when responding to SF-36 questions pertaining to health, physical health, pain, and bodily pain. METHODS: The wording of several SF-36 questions were adapted for a headache population by making specific reference to "headaches" when asking people to rate the impact of health issues on their life. The results of the modified "Headache" SF-36 were compared with a similar population of transformed migraine patients who had completed the "Standard" SF-36. RESULTS: Significant differences were found between scores for the "Standard" SF-36 group and the "Headache" SF-36 group across all SF-36 variables except for "General Health." CONCLUSIONS: Misinterpretation of the concepts of "health,""physical health,""pain," and "bodily pain," although commonly used by the SF-36 in many populations, could influence responses on this measure, as respondents may not relate their head/headaches to these constructs. To ensure that accurate data are obtained in relation to the quality of life of headache patients, consideration should be given to using a form of the SF-36 that has been modified to allow appropriate interpretation of the questions completed by headache 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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".