Preoperative Acetaminophen For Microsuspension Laryngoscopy Reduces Postoperative Opioid Use
Bibliographic record
Abstract
OBJECTIVES: The opioid crisis has prompted consideration of analgesic prescriptions. This study explored the value of preoperative acetaminophen for pain control following microsuspension laryngoscopy (MSL) and compared the results with a previous study of pain and opioid use following MSL (Tsang et al.). METHODS: A prospective open-label clinical trial was conducted in patients undergoing MSL. All patients were administered preoperative acetaminophen. Short-form McGill Pain Questionnaire (SF-MPQ), pain visual analogue scale (VAS), and present pain intensity (PPI) scores were collected preoperatively and on postoperative days (PODs) 1, 3, 7, and 14. Statistical analysis identified variables associated with opioid use or increased pain scores, and compared outcomes with Tsang et al. RESULTS: Eighty-nine patients were included (mean age 52.8 ± 17.3 years, 40 males). All patients received preoperative 1 g acetaminophen (77 (86.5%) orally) with no adverse effects. On POD1, opioid usage was 10%. Median [IQR] pain scores were 5 [2-11], 21 [12.3-56.8], and 3 [2-3.3] on SF-MPQ, VAS, and PPI, respectively. Post-Anesthesia Care Unit (PACU) opioid requirements significantly correlated with POD1 opioid consumption (τb = 0.214; p ≤ 0.05), and significant associations with PACU opioid administration were found for total anesthesia time (OR (95%CI) = 1.271 (1.043-1.548), p = 0.017) and total laryngoscope suspension time (OR (95%CI) = 0.791 (0.651-0.962, p = 0.019)). This cohort demonstrated reduced opioid usage on POD1 compared with Tsang et al (23%). CONCLUSIONS: Preoperative acetaminophen is a safe intervention, resulting in decreased postoperative opioid use following MSL. Anesthesia time correlated with need for postoperative opioids. LEVEL OF EVIDENCE: 4 Laryngoscope, 134:4625-4635, 2024.
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".