Demographics, Pain Characteristics and Diagnostic Classification Profile of Chronic Non-Cancer Pain Patients Attending a Canadian University-Affiliated Community Pain Clinic
Bibliographic record
Abstract
INTRODUCTION: Little information exists regarding the characteristics of patients with chronic non-cancer pain (CNCP) attending Canadian pain clinics. The study describes the demographics, pain characteristics and the diagnostic classification profile of such patients attending a university-affiliated community-based pain clinic in the Greater Toronto Area. METHODS: Retrospective descriptive study based on 644 unique consecutive CNCP patients assessed between January 2016 and December 2017. RESULTS: The female/male ratio was 1.6:1; 80% were younger than 65 years; 43% held some form of employment (full-time, part-time or self employment); median pain duration was 3 years; car accidents and medical conditions accounted for 28 and 27% of pain onset, respectively; 34% had four or more distinct areas of pain; and low back pain (LBP) was the most prevalent site (66%), but was the sole site of pain in less than a third of these patients. Age was positively associated with LBP prevalence. Self-reported health service utilization (visits to the emergency room, pain physician or psychologist) increased with patient psychopathology. Cannabis was used by 15% of the cohort and opioids by 34.5%, with only one in six opioid users exceeding 90 mg of morphine equivalent dose per day. Comparison of our data to three previously published studies from other Canadian pain clinics demonstrated both similarities and substantial differences between the populations. CONCLUSION: Our study highlights regional differences between CNCP population phenotypes. Recognition of biomedical, psychological and socio-environmental factors affecting pain should be considered for patient stratification and rational approaches to treatment, as "one size treatment does not fit all".
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".