The Canadian version of the National Institutes of Health minimum dataset for chronic low back pain research: reference values from the Quebec Low Back Pain Study
Bibliographic record
Abstract
ABSTRACT: The National Institutes of Health (NIH) minimum dataset for chronic low back pain (CLBP) was developed in response to the challenge of standardizing measurements across studies. Although reference values are critical in research on CLBP to identify individuals and communities at risk of poor outcomes such as disability, no reference values have been published for the Quebec (Canada) context. This study was aimed to (1) provide reference values for the Canadian version of the NIH minimum dataset among individuals with CLBP in Quebec, both overall and stratified by gender, age, and pain impact stratification (PIS) subgroups, and (2) assess the internal consistency of the minimum data set domains (pain interference, physical function, emotional distress or depression, sleep disturbance, and PIS score). We included 2847 individuals living with CLBP who completed the baseline web survey of the Quebec Low Back Pain Study (age: 44.0 ± 11.2 years, 48.1% women) and were recruited through social media and healthcare settings. The mean score was 6.1 ± 1.8 for pain intensity. Pain interference, physical function, emotional distress or depression, sleep disturbance, and PIS scores were 12.9 ± 4.1, 14.4 ± 3.9, 9.8 ± 4.4, 13.0 ± 3.6, and 26.4 ± 6.6, respectively. Emotional distress or depression showed floor effects. Good-to-excellent internal consistency was found overall and by language, gender, and age subgroups for all domains (alpha: 0.81-0.93) and poor-to-excellent internal consistency for PIS subgroups (alpha: 0.59-0.91). This study presents reference values and recommendations for using the Canadian version of the NIH minimum dataset for CLBP that can be useful for researchers and clinicians.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".