The development of a nomogram to determine the frequency of elevated risk for non-medical opioid use in cancer patients
Bibliographic record
Abstract
OBJECTIVE: Non-medical opioid use (NMOU) is a growing crisis. Cancer patients at elevated risk of NMOU (+risk) are frequently underdiagnosed. The aim of this paper was to develop a nomogram to predict the probability of +risk among cancer patients receiving outpatient supportive care consultation at a comprehensive cancer center. METHOD: 3,588 consecutive patients referred to a supportive care clinic were reviewed. All patients had a diagnosis of cancer and were on opioids for pain. All patients were assessed using the Edmonton Symptom Assessment Scale (ESAS), Screener and Opioid Assessment for Patients with Pain (SOAPP-14), and CAGE-AID (Cut Down-Annoyed-Guilty-Eye Opener) questionnaires. "+risk" was defined as an SOAPP-14 score of ≥7. A nomogram was devised based on the risk factors determined by the multivariate logistic regression model to estimate the probability of +risk. RESULTS: 731/3,588 consults were +risk. +risk was significantly associated with gender, race, marital status, smoking status, depression, anxiety, financial distress, MEDD (morphine equivalent daily dose), and CAGE-AID score. The C-index was 0.8. A nomogram was developed and can be accessed at https://is.gd/soappnomogram. For example, for a male Hispanic patient, married, never smoked, with ESAS scores for depression = 3, anxiety = 3, financial distress = 7, a CAGE score of 0, and an MEDD score of 20, the total score is 9 + 9+0 + 0+6 + 10 + 23 + 0+1 = 58. A nomogram score of 58 indicates the probability of +risk of 0.1. SIGNIFICANCE OF RESULTS: We established a practical nomogram to assess the +risk. The application of a nomogram based on routinely collected clinical data can help clinicians establish patients with +risk and positively impact care planning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".