Efficacy of postoperative pain management in head and neck cancer patients
Bibliographic record
Abstract
BACKGROUND: Our study quantifies the effectiveness of perioperative pain control in a cohort of patients undergoing major head and neck surgery with free flap reconstruction. Our long-term goal is to improve pain control and thereby increase mobility, decrease postoperative complications and decrease hospital stay. METHODS: A retrospective analysis was performed at a tertiary, academic head and neck surgical oncology program in Calgary, Alberta, Canada from January 1, 2015 - December 31, 2015. Pain scores were recorded prospectively. Primary outcomes were frequency of postoperative pain assessments and pain intensity using the numeric rating scale. RESULTS: The cohort included 41 patients. Analysis was limited to pain scores recorded from postoperative days 1-14. There was an average of 7.3 pain measurements per day (SD 4.6, range 1-24) with the most frequent monitoring on postoperative days 1-4. Median pain scores ranged from 0 to 4.5 with the highest median score on postoperative day 6. The daily maximum pain scores recorded ranged from 8 to 10 with scores of 10 recorded on postoperative days 1, 2, 3, 5, 7, 8, and 10. Patients most frequently had inadequate pain control on postoperative days 1, 2, 4, and 5 with the majority occurring on postoperative day 1. CONCLUSIONS: Postoperative pain control could be improved at our centre. The frequency of pain assessments is also highly variable. Ongoing measurement, audit, and feedback of analgesic protocol effectiveness is an excellent first step in improving perioperative pain management in patients undergoing major head and neck cancer surgery with free flap reconstruction.
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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 | 0.006 |
| 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) | 0.001 | 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".