Neck pain patients’ preference scores for their current health
Bibliographic record
Abstract
PURPOSE: To elicit neck pain (NP) patients' preference scores for their current health, and investigate the association between their scores and NP disability. METHODS: Rating scale scores (RSs) and standard gamble scores (SGs) for current health were elicited from chronic NP patients (n=104) and patients with NP following a motor vehicle accident (n=116). Patients were stratified into Von Korff Pain Grades: Grade I (low-intensity pain, few activity limitations); Grade II (high-intensity pain, few activity limitations); Grade III (pain with high disability levels, moderate activity limitations); and Grade IV (pain with high disability levels, several activity limitations). Multivariable regression quantified the association between preference scores and NP disability. RESULTS: Mean SGs and RSs were as follows: Grade I patients: 0.81, 0.76; Grade II: 0.70, 0.60; Grade III: 0.64, 0.44; Grade IV: 0.57, 0.39. The association between preference scores and NP disability depended on type of NP and preference-elicitation method. Chronic NP patients' scores were more strongly associated with depressive symptoms than with NP disability. In both samples, NP disability explained little more than random variance in SGs, and up to 51% of variance in RSs. CONCLUSION: Health-related quality-of-life is considerably diminished in NP patients. Depressive symptoms and preference-elicitation methods influence preference scores that NP patients assign to their health.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".