Pain Characteristics and Demographics of Patients Attending a University‐Affiliated Pain Clinic in Toronto, Ontario
Bibliographic record
Abstract
BACKGROUND: Pain clinics tend to see more complex chronic pain patients than primary care settings, but the types of patients seen may differ among practices. OBJECTIVE: The aim of the present observational study was to describe the pain and demographic characteristics of patients attending a university-affiliated tertiary care pain clinic in Toronto, Ontario. METHODS: Data were collected on 1242 consecutive new patients seen over a three-year period at the Comprehensive Pain Program in central Toronto. RESULTS: Musculoskeletal problems affecting large joints and the spine were the predominant cause of pain (more prevalent in women), followed by neuropathic disorders (more prevalent in men) in patients with recognizable physical pathology. The most affected age group was in the 35- to 49-year age range, with a mean pain duration of 7.8 years before the consultation. While 77% of the Comprehensive Pain Program patients had relevant and detectable physical pathology for pain complaints, three-quarters of the overall study population also had significant associated psychological or psychiatric comorbidity. Women, in general, attended the pain clinic in greater numbers and had less apparent physical pathology than men. Finally, less than one in five patients was employed at the time of referral. CONCLUSIONS: The relevance of the data in relation to other pain clinics is discussed, as well as waiting lists and other barriers faced by chronic pain patients, pain practitioners and pain facilities in Ontario and Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".