Hypertension, chronic obstructive pulmonary disease, diabetes and depression among older methadone maintenance patients in <scp>B</scp>ritish <scp>C</scp>olumbia
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Risk factors in older methadone maintenance treatment (MMT) patients may put them at a greater risk of acquiring chronic diseases; however, this group might experience barriers to treatment resulting in reduced recommended prescriptions. The research objective for this study was to assess whether MMT patients were significantly different from a matched control group in terms of medications dispensed for hypertension, chronic obstructive pulmonary disease (COPD), diabetes and depression. DESIGN AND METHODS: The research design was a case-control study, where prescription claims data from the British Columbia database were used. MMT patients 50 years of age and older were randomly selected, and control subjects were individually matched in terms of age, sex, social assistance coverage and geographic jurisdiction. RESULTS: Each group consisted of 199 participants. Odds ratios (OR) were calculated to compare the odds of MMT patients to non-MMT patients on a first-line medication for each chronic disease under investigation. The MMT group was significantly more likely to receive medications for COPD (OR = 32.68, P < 0.001) and depression (OR = 4.07, P < 0.001), and no significant differences for hypertension (OR = 0.86) or diabetes (OR = 0.74). DISCUSSION: Higher rates of COPD among MMT clients is likely explained by elevated smoking, and higher rates of depression may be explained by multiple disadvantages associated with substance use. Although the groups were similar for diabetes prescriptions, the MMT group likely experienced barriers to receiving treatment since prior research suggests their rates should be elevated due to methadone use.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".