Methadone Patients Satisfaction with Quality of Life - A Clinical Correlation
Bibliographic record
Abstract
Background: Chronic pain disrupts sleep and has been identified as a symptom that impairs quality of life.Objectives: Evaluate how subjective satisfaction of life correlates with clinically known parameters such as sleep patterns, pain levels, infectious disease status (Heptatitis C and HIV), urine screening for benzodiazepines, cocaine, and morphine, and methadone dose in patients receiving methadone maintenance therapy.Methods: De-identified and retrospective clinical data was extracted from a Canadian methadone clinic.Data including insomnia severity index, brief pain inventory, satisfaction with quality of life, outcomes of urine screening tests, and hepatitis C and HIV status.Results: Number of years of opiate use was positively correlated with average pain levels (r=0.39).Average pain levels (r=.29) and worst pain levels (r=.28) were positively correlated to insomnia.Level of satisfaction with life is inversely correlated with insomnia(r=-0.37).The total score on the insomnia index was inversely correlated to hepatitis C status (r=-.32).Conclusion: Patients who had been using opiates for longer periods reported higher levels of pain.Patients with higher levels of pain reported more symptoms of insomnia.Patients with more symptoms of insomnia were less satisfied with their quality of life.Those positive for hepatitis C reported lower levels of insomnia.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".