Unveiling Prescribing Patterns: A Systematic Review of Chronic Opioid Prescriptions After Head and Neck Cancer Surgeries
Bibliographic record
Abstract
OBJECTIVE: This study aims to review opioid prescribing changes for pain management in head and neck cancer (HNC) surgery patients, given the recent focus on Enhanced Recovery After Surgery protocols. DATA SOURCES: MEDLINE, Embase, and CENTRAL, covering 1998 to 2023. REVIEW METHODS: We selected studies that evaluated opioid prescribing patterns post-major HNC surgery in various settings, including tertiary care hospitals and community hospitals. Primary outcomes considered were prevalence and patterns of opioid use post-surgery, as well as related outcomes such as chronic use and side effects. RESULTS: Of 1278 abstracts, 24 studies involving 17,027 patients from the United States, China, and Canada met inclusion criteria. Quality was assessed using the MINORS scale, with an average score of 9.9 for non-comparative studies and 20.0 for comparative studies. Persistent opioid use post-surgery, defined as ongoing prescriptions 90 days after treatment, was noted in 15.4% to 64% of patients. Two studies reported adverse events, with up to 16% of patients experiencing side effects. Risk factors for chronic use included preoperative opioid use, tobacco use, higher cancer stage, adjuvant treatment, and demographic factors. Correlations were found between larger opioid prescriptions and shorter survival in advanced cancers. There was notable variability in patient-reported pain control. CONCLUSION: Persistent opioid use post-HNC surgery is common, with variable efficacy and risk of adverse effects. Tailoring pain management to individual risk factors and focusing on multimodal analgesia could reduce the risks of continued opioid use. Future prospective studies are required to identify optimal pain management strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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".