Can Headache Profile Predict Future Disability
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine if headache profile can predict future disability in patients with tension-type headache (TTH). MATERIALS AND METHODS: Eighty-three individuals with TTH were recruited. To be included in the study participants needed to fulfill the International Headache Society classification's criteria for episodic or chronic TTH form and to be at least 18 years old. Baseline clinical outcomes (headache and neck-related disability, kinesiophobia, self-efficacy, and anxiety) and physical outcomes (neck extensors muscles maximum voluntary contraction) were collected for all participants. A prospective data collection of headache characteristics (intensity and frequency) was conducted using daily SMS or e-mail over a 1-month period. Headache-related disability was assessed at the 3-month follow-up and was used as the disability criterion for TTH. RESULTS: Correlations showed that the number of years with headache (r=0.53, P<0.001), self-reported neck pain intensity (r=0.29, P=0.025), headache frequency (r=0.60, P<0.001) and intensity (r=0.54, P<0.001), anxiety (r=0.28; P=0.031), as well as neck-related disability (r=0.64, P<0.001) were correlated to headache-related disability assessed at 3 months. Multiple regression showed that these determinants can be used to predict headache disability (R =0.583). Headache frequency (β=0.284) was the best individual predictor. DISCUSSION: Results showed that TTH frequency and intensity and the presence of concomitant infrequent migraine are predictors of future disability over a 3-month period. Further studies are needed to evaluate the contribution of other potential physical outcomes on headache-related disability.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".