An association between tobacco smoking, daily opioid use, pain response and the risk for aberrant opioid use behaviors.
Bibliographic record
Abstract
226 Background: Opioid misuse is a growing crisis. Cancer patients at risk of aberrant drug behaviors are frequently underdiagnosed. Primary aim was to determine the association between current tobacco smoking, daily opioid use, pain response, and risk for aberrant opioid use behaviors (AOB) among patients receiving outpatient supportive care consultation at a comprehensive cancer center. Methods: 1,501 consecutive cancer patients referred to a supportive care clinic from March 1, 2016 to June 6, 2018 were reviewed. Patients were eligible if they had diagnosis of cancer, and were on opioids for pain for at least a week. All patients were assessed using Edmonton Symptom Assessment Scale (ESAS), SOAPP-14 (validated questionnaire for assessment of risk for aberrant opioid use behaviors), and CAGE-AID. Patients with AOB (SOAPP+) were defined by SOAPP score ≥7. Results: Median age was 61yrs. Median ESAS pain item score on consultation was 5. Median ECOG was 2. Never smokers, previous smokers, and current smokers were 54%, 40%, and 6.6% respectively. 16.8% were AOB+ and 10.1% were CAGE-AID+. There was no significant difference in ESAS pain response at first follow-up between AOB+ versus AOB negative. Morphine Equivalent Daily Dose (MEDD) in AOB+ never smokers versus previous smokers versus current smokers was 45 mg versus 60 mg versus 75mg respectively (P=0.03). Table 1 shows that male pts and those with current or previous smoking history, anxiety, and prior alcoholism/illicit drug use are at increased risk of AOB. Conclusions: Male patients and those with history of current or previous smoking history, anxiety, and prior alcoholism/illicit drug use are at increased risk of AOB. [Table: see text]
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".