Characteristics of Chronic Noncancer Pain Patients Assessed with the Opioid Risk Tool in a Canadian Tertiary Care Pain Clinic
Bibliographic record
Abstract
BACKGROUND: The Opioid Risk Tool (ORT) is a screening instrument for assessing the risk of opioid-related aberrant behavior in chronic noncancer pain (CNCP) patients. OBJECTIVE: This study aims to compare patient characteristics documented in the original ORT study with those identified in CNCP patients assessed using a physician-administered ORT in a tertiary care pain clinic in Toronto, Canada. METHODOLOGY: This was a descriptive cross-sectional study of 322 consecutive new patients referred over 12 months. Data extraction included ORT scores, demographics, pain ratings, opioid, and other medication use at point of entry, diagnosis, and other variables. Characteristics were compared with those described in the original ORT study. RESULTS: The total mean ORT scores of patients in this study were related to several demographic (gender, age, marital status, and country of birth) and nondemographic variables (employment status, cigarette smoking, and contribution of biomedical and/or psychological factors to presentation). Prevalence of characteristics noted in this patient sample differed substantially from that found in Webster and Webster as the basis for ORT scores. CONCLUSION: Significant differences existed between this study population and the patient sample from which the ORT was derived. Limitations of this study are discussed. We concur with the authors of the original study that the ORT may not be applicable in different pain populations and settings. Based on our findings, we encourage caution in interpreting the ORT in general CNCP settings until further studies are performed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".