Efficacy of Multimodal Analgesia for Postoperative Pain Management in Head and Neck Cancer Patients
Bibliographic record
Abstract
Postoperative opioid use has been linked to the subsequent development of opioid dependency. Multimodal analgesia (MMA) can reduce the use of opioids in the postoperative period, but MMA has not been well-studied after major head and neck surgery. Our goal is to explore the association between MMA and postoperative opioid use and pain control in patients undergoing major head and neck surgery. We performed a retrospective study in adult (age ≥ 18 years) patients undergoing primary head and neck cancer resection with free-flap reconstruction. All patients were treated using an established care pathway. The baseline group was treated between January 2015–December 2015 (n = 41), prior to the implementation of MMA, and were compared to an MMA-treated cohort treated between December 2017–June 2019 (n = 97). The primary outcome was the proportion of opioids prescribed and oral morphine equivalents (OMEs) consumed during the hospitalization. The secondary outcome was pain control. We found that the post-MMA group consumed fewer opioids in the postoperative period compared to the pre-MMA group. Prior to post-operative day (POD) 6, pain control was better in the post-MMA group; however, the pain control lines intersect on POD 6 and the pre-MMA group appeared to have better pain control from PODs 7–10. In conclusion, our data suggest MMA is an effective method of pain control and opioid reduction in patients undergoing surgery for head and neck cancer with free flap reconstruction. MMA use was associated with a significant decrease in the quantity of opioids consumed postoperatively. The MMA protocol was associated with improved pain management early in the postoperative course. Finally, the MMA protocol is a feasible method of pain control and may reduce the adverse side effects associated with opioid use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".