The Canadian minimum dataset for chronic low back pain research: a cross-cultural adaptation of the National Institutes of Health Task Force Research Standards
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".