Is topical lidocaine beneficial before catheter insertion in esophageal manometry and ambulatory pH monitoring?
Bibliographic record
Abstract
BACKGROUND: Conventionally, topical anesthesia is applied to improve the tolerance of esophageal manometry (EM) and ambulatory pH monitoring (apH) but there is presently no evidence supporting this practice. We aimed to compare the tolerance of EM and apH with vs without topical lidocaine anesthesia. METHODS: A prospective study was conducted at our center between January 2017 and January 2019. All patients who underwent EM or apH and completed a systematically distributed standardized patient survey were included. From January 2017 to June 2018, all patients had a viscous lidocaine solution applied before EM and apH ("lidocaine" group). After June 2018, we ceased applying any topical anesthesia ("no lidocaine" group). Patient-reported adverse effects and satisfaction scores were compared between these two patient groups. KEY RESULTS: Two hundred forty-nine patient surveys were included. "Lidocaine" (n = 124) and "no lidocaine" (n = 125) groups were similar in age (56.9 ± 14.0 vs 56.0 ± 13.7; P = .77) and gender distributions (65.9% vs 63.3% female; P = .68). Patients in the "lidocaine" group were less likely to report pain during catheter insertion (33.6% vs 50.8%; P = .007, OR: 0.49 [95% CI 0.29-0.83]) and reported a lower overall pain score (2.82 ± 1.38 vs 3.20 ± 1.42 on 5; P = .04). There was a tendency toward increased global satisfaction with lidocaine application but that was not statistically significant (4.36 ± 1.05 vs 4.11 ± 1.13; P = .08). In subgroup analyses, female patients, younger patients, and patients who underwent EM were more likely to benefit from lidocaine application. CONCLUSIONS AND INFERENCES: Application of topical lidocaine before esophageal motility tests reduces pain during catheter insertion and overall 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".