Examination of the Screener and Opioid Assessment for Patients with Pain-Short Form (SOAPP-SF) in an oncology palliative medicine clinic.
Bibliographic record
Abstract
196 Background: The National Comprehensive Cancer Network states opioids can be used to treat cancer pain and prescribers should identify patients at risk for opioid misuse; research in this area is limited. In the non-cancer population, SOAPP-SF is a validated tool to predict aberrant drug behavior; a score of ≥ 4 (out of 20) is considered high risk. We performed a retrospective observational study to determine the utility of the SOAPP in identifying opioid misuse in the oncology population as measured by a non-compliant toxicology screen. Methods: Consecutive consults seen during a 6-month period completed the 5-question SOAPP-SF and Edmonton Symptom Assessment System (ESAS) form. Toxicology screens assessed non-compliance (i.e., absence of prescribed medications and/or presence of non-prescribed or illegal substances). Logistic regression models estimated the associations of composite and individual SOAPP-SF scores and ESAS symptom scores with non-compliant screens. Threshold analysis were conducted to identify an optimal SOAPP-SF cutoff. Results: Of 192 consults, 64 patients providing SOAPP-SF score and toxicology screen were evaluable. Mean age was 59 ± 9.8 years: 56% were female, 34% and 62% were African American and Caucasian respectively. Median SOAPP-SF score was 2 (range: [0, 12]). Non-compliant screens were observed in 31% of patients. The area under the curve (AUC) was 0.65. The validated SOAPP-SF cutoff score of ≥ 4 was associated with a sensitivity and specificity of 0.43 and 0.79, respectively (p = 0.082). Sensitivity (0.76) and specificity (0.72) were maximized at a cutoff score of ≥ 3 (p < 0.001). When evaluated individually, the SOAPP-SF question about smoking habit was associated with a non-compliant screen (p = 0.020). Increased ESAS pain scores were associated with SOAPP-SF score ≥ 3 (p = 0.013). Conclusions: SOAPP-SF can identify oncology patients at risk for opioid misuse. Preliminary analyses suggest a more appropriate threshold of identification is a score of ≥ 3 not ≥ 4. Future work will increase numbers of evaluable patients and examine other factors associated with opioid misuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".