The Canadian minimum dataset for chronic low back pain research: a cross-cultural adaptation of the National Institutes of Health Task Force Research Standards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To better standardize clinical and epidemiological studies about the prevalence, risk factors, prognosis, impact and treatment of chronic low back pain, a minimum data set was developed by the National Institutes of Health (NIH) Task Force on Research Standards for Chronic Low Back Pain. The aim of the present study was to develop a culturally adapted questionnaire that could be used for chronic low back pain research among French-speaking populations in Canada. METHODS: The adaptation of the French Canadian version of the minimum data set was achieved according to guidelines for the cross-cultural adaptation of self-reported measures (double forward-backward translation, expert committee, pretest among 35 patients with pain in the low back region). Minor cultural adaptations were also incorporated into the English version by the expert committee (e.g., items about race/ethnicity, education level). RESULTS: This cross-cultural adaptation provides an equivalent French-Canadian version of the minimal data set questionnaire and a culturally adapted English-Canadian version. Modifications made to the original NIH minimum data set were minimized to facilitate comparison between the Canadian and American versions. INTERPRETATION: The present study is a first step toward the use of a culturally adapted instrument for phenotyping French- and English-speaking low back pain patients in Canada. Clinicians and researchers will recognize the importance of this standardized tool and are encouraged to incorporate it into future research studies on chronic low back pain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it