A Randomized Controlled Pilot Study of Topical Ropivacaine for Prevention of Post-POEM Pain
Bibliographic record
Abstract
Background and Aims: Although usually mild to moderate in severity, postoperative pain after peroral endoscopic myotomy (POEM) is common. There are no studies that have addressed minimizing postoperative pain in patients undergoing POEM for achalasia. We hypothesized that intraoperative topical intra-tunnel irrigation with ropivacaine would result in a significant reduction in pain scores in the postoperative period. Methods: A double-blind, randomized, placebo-controlled trial was conducted at the Kingston Health Sciences Center. Patients received either 30 mL of 0.2% ropivacaine or 30 mL of placebo irrigated topically into the POEM tunnel after completing the myotomy and prior to closing the mucosal incision. The primary outcome was pain post-POEM at 6 h assessed by the Numeric Rating Scale (NRS). Secondary objectives included assessing pain score at 0.5, 1, 2, 4 h post-POEM and on discharge, Quality of Recovery (QoR-15) scores at discharge, narcotic requirement, adverse events, and patients' willingness to have the procedure done on an outpatient basis. Results: = 0.171). No statistical difference was seen in the pain scores. Overall usage of post-procedural narcotics was low with no differences between the two groups. Fifty percent of patients in both groups were willing to have the procedure done as an outpatient. Conclusion: The addition of intra-procedural tunnel irrigation with 30 mL 0.2% ropivacaine did not lead to reduced post-POEM pain.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".