Opioid disposal rates after spine surgery
Bibliographic record
Abstract
BACKGROUND: Diversion of prescription opioids pills is a significant contributor to opioid misuse and the opioid epidemic. The goal of this study was to determine the frequency and quantity of excess opioid pills among patients undergoing spine surgery. Further, we wanted to determine the frequency of appropriate opioid disposal. METHODS: This was a prospective cohort study of patients undergoing elective spine surgery within a multi-hospital, academic, urban university health system enrolled in a text-messaging program used to track postoperative opioid disposal. Patients who self-reported discontinuation of opioid use but with leftover pills were contacted via telephone and surveyed on opioid disposal. RESULTS: Of the 291 patients who enrolled in the text-messaging program, 192 (66%) patients reported discontinuing opioids within 3 months of surgery. Although 76 (40%) reported excess opioid pills after cessation of use, only 47 (62%) participated in the telephone survey regarding opioid disposal. The median number of leftover pills among these 47 patients was 5 (5, 15) and 64% had not disposed of their prescription. CONCLUSION: Among the 47 telephone survey participants, a persistent gap remained in postoperative opioid excess and improper disposal. Future efforts must focus on initiatives to improve opioid disposal rates to reduce the quantity of opioids at risk for diversion and to reduce excess prescribing.
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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 | 0.001 | 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) | 0.003 | 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".