Characteristics of chronic pain patients in a rural teaching practice.
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics of chronic noncancer pain (CNCP) patients taking oxycodone or its derivatives in a rural teaching practice. DESIGN: Characteristics of CNCP patients taking oxycodone over a 5-year period (September 2003 to September 2008) were compared with those of patients not taking opioid medications using a retrospective chart audit. SETTING: A rural teaching practice in southwestern Ontario. PARTICIPANTS: A total of 103 patients taking chronic oxycodone therapy for CNCP and a random sample of 104 patients not taking opioid medication. MAIN OUTCOME MEASURES: Number of visits, health problems, sex, and previous history of addiction and mental illness. RESULTS: Patients with CNCP taking oxycodone had significantly more health problems (P < .001), including drug and tobacco addictions. They had more than 3 times as many clinic visits during the same period of time as patients not taking opioid medication (mean of 39.0 vs 12.8 visits, P < .001). CONCLUSION: Patients with CNCP in this rural teaching practice had significantly more health issues (P < .001) and were more likely to have a history of addiction than other patients were. They created more work with significantly more visits over the same period compared with the comparison group.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".