Contribution of psychiatric diagnoses to extent of opioid prescription in the first year post‐head and neck cancer diagnosis: A longitudinal study
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine, within the first-year post-head and neck cancer (HNC) diagnosis, the contribution of past and upon HNC psychiatric diagnoses (ie, substance use disorder, major depressive disorder, and anxiety disorder) to the extent (ie, cumulated dose) of opioid prescription. METHODS: Prospective longitudinal study of 223 consecutive adults (on 313 approached; 72% participation) newly diagnosed (<2 weeks) with a first occurrence of primary HNC, including Structured Clinical Interviews for DSM-IV disorders, validated psychometric measures, and medical chart reviews. Opioid doses were translated into standardized morphine milligram equivalents (MME) using CDC guidelines. A model of variables was tested using multiple linear regression. RESULTS: Fifty-five percent (123/223) of patients received opioids at some point during the first 12 months post-HNC diagnosis, 37.7% (84/223) upon HNC diagnosis (pre-treatment), 40.8% (91/223) during treatments, and 31.4% (70/223) post-treatment. The multiple linear regression indicated that an AD (P = 0.04) upon HNC diagnosis in early stage contributes to cumulated MME dose in the first year post-HNC diagnosis. CONCLUSION: This study underlines how anxiety has important repercussions on the management of pain and illustrates the importance of screening for AD upon HNC diagnosis to allow for early prophylactic treatment and support.
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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) | 0.000 | 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".