Opioid Risk Screening in an Oncology Palliative Medicine Clinic
Bibliographic record
Abstract
PURPOSE: Little information exists on factors that predict opioid misuse in oncology. We adopted the Screener and Opioid Assessment for Patients With Pain-Short Form (SOAPP-SF) and toxicology testing to assess for opioid misuse risk. The primary objective was to (1) identify characteristics associated with a high-risk SOAPP-SF score and noncompliant toxicology test, and (2) determine SOAPP-SF utility to predict noncompliant toxicology tests. METHODS: From July 1, 2017, to December 31, 2017, new patients completed the Edmonton Symptom Assessment Scale (ESAS), SOAPP-SF, and narcotic use agreement. Toxicology test results were collected at subsequent visits. RESULTS: Of 223 distinct patients, 96% completed SOAPP-SF. Mean age was 61 ± 12.7 years, 58% were female, 68% were White, and 28% were Black. Eighty-three eligible patients (38%) completed toxicology testing. Younger age, male sex, and increased ESAS depression scores were associated with high-risk SOAPP-SF scores. Smoking habit was associated with an aberrant test. An SOAPP-SF score ≥ 3 predicted a noncompliant toxicology test. CONCLUSION: Male sex, young age, and higher ESAS depression score were associated with a high SOAPP-SF score. Smoking habit was associated with an aberrant test. An SOAPP-SF of ≥ 3 (sensitivity, 0.74; specificity, 0.64), not ≥ 4, was predictive of an aberrant test; however, performance characteristics were decreased from those published by Inflexxion, for ≥ 4 (sensitivity, 0.86; specificity, 0.67). The specificity warrants caution in falsely labeling patients. The SOAPP-SF may aid in meeting National Comprehensive Cancer Network recommendations to screen oncology patients for opioid misuse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".