Is the Quebec provincial administrative database a valid source for research on chronic non‐cancer pain?
Bibliographic record
Abstract
PURPOSE: The objective of this study was to evaluate the validity of diagnostic codes recorded in the Régie de l'assurance maladie du Québec (RAMQ) administrative database for identifying patients suffering from various types of chronic non-cancer pain. METHODS: The validity of published International Classification of Diseases, Ninth Revision, coding algorithms for identifying patients with particular chronic pain syndromes in the RAMQ database was tested using pain specialist-established diagnostic data of 561 patients enrolled in the Quebec Pain Registry, which was used as the reference standard. Modified versions of these algorithms (i.e., adaptation of the number of healthcare encounters) were also tested. For each algorithm, sensitivity, specificity, positive/negative predictive values, and their respective 95% confidence intervals (95%CI) were calculated. RESULTS: In the RAMQ database, some previously published algorithms and modified versions of these algorithms were found to be valid for identifying patients suffering from chronic lumbar pain (sensitivity: 0.65, 95%CI: 0.59-0.71; specificity: 0.83, 95%CI: 0.79-0.87), chronic back pain (sensitivity: 0.70, 95%CI: 0.64-0.76; specificity: 0.73, 95%CI: 0.68-0.78), and chronic neck/back pain (sensitivity: 0.71, 95%CI: 0.65-0.76; specificity: 0.78, 95%CI: 0.72-0.82). Algorithms to identify patients with other types of chronic pain showed low sensitivity: complex regional pain syndrome (≤0.07), fibromyalgia (≤0.42), and neuropathic pain (≤0.39). CONCLUSIONS: Our study provides evidence supporting the value of the RAMQ administrative database for conducting research on certain types of chronic pain disorders including back and neck pain. Users should, however, be cautious about the limitations of this database for studying other types of chronic pain syndromes such as complex regional pain syndrome, fibromyalgia, and neuropathic pain.
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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 | 0.023 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".