Opioid therapy vs. multimodal analgesia in head and neck Cancer (OPTIMAL-HN): study protocol for a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Radiation-induced mucositis (RIM) pain confers substantial morbidity for head and neck cancer (HNC) patients undergoing radiotherapy alone (RT) or chemoradiotherapy (CRT), often reducing treatment compliance. However, no standard currently exists for the treatment of RIM, and high dose opioid therapy, with its associated side effects and increased risk for chronic opioid use, remains the cornerstone of HNC pain management. The goal of this randomized clinical trial is to compare multimodal analgesia using analgesic medications with different mechanisms of action, to the institutional standard of opioid analgesia alone, in order to ascertain the optimal analgesic regimen for the management of RIM pain in HNC patients. METHODS: In this open-label, single-institution, non-inferiority, randomized clinical trial, sixty-two patients with mucosal head and neck malignancies treated with curative-intent radiation will be randomized in a 1:1 ratio, stratified by RT or CRT, between Arm 1: opioid analgesia alone as per the institutional standard, or Arm 2: multimodal analgesia using Pregabalin, Acetaminophen, and Naproxen, in addition to opioids, if required. The primary endpoint is the average 11-Numeric Rating Scale (11-NRS) score for pain during the last week of radiation treatment. Secondary endpoints include: average weekly opioid use, duration of opioid requirement, average daily 11-NRS score for pain, average weekly opioids dispensed, quality of life, hospitalizations for analgesic medication-induced complications, time to feeding tube insertion, weight loss, toxicity, treatment interruptions, and death within 3 months of completing RT treatment. Patients are eligible once analgesia is required for moderate 4/10 pain. DISCUSSION: This study will assess the efficacy and safety of multimodal analgesia and its impact on opioid requirements, clinical outcomes, and quality of life, as a potential new standard treatment for RIM pain in HNC patients undergoing definitive RT or CRT. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04221165 . Date of registration: January 9, 2020. Appendix 2 reports the World Health Organization trial registration dataset.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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".