Acupuncture for Relief of Gag Reflex in Patients Undergoing Transoesophageal Echocardiography—A Protocol for a Randomized Placebo-Controlled Trial
Bibliographic record
Abstract
Background: Gagging during transesophageal echocardiography examination (TEE) can be distressing and even dangerous for patients. The needling of acupuncture point CV24 was described to be effective in reducing the gag reflex during TEE in patients with ischemic stroke or transient ischemic attack. Methods: We describe a proposal for a prospective, randomized, patient, practitioner and assessor-blinded, single-center trial with two arms/groups; real acupuncture will be compared to placebo acupuncture. A total of 60 (30 per group) patients scheduled for elective TEE in order to exclude a cardiac embolic source, endocarditis or for valve failure evaluation will be recruited according to patients’ selection criteria and receive either indwelling fixed intradermal needles at acupoints CV24 and bilateral PC6 or placebo needles at the same areas. Patients, the practitioners who will perform the TEE procedure, and the assessor of the outcome measures will be unaware of the group’s (real or placebo) allocation. Results: The primary outcome is the intensity of gagging, measured using verbal rating scale (VRS-11) from 0 = no gagging to 10 = intolerable gagging. Secondary outcomes include the incidence of gagging, the use of rescue medication, patients’ satisfaction with relief of unwanted side effects during TEE procedure, success of patients’ blinding (patients’ opinion to group allocation), heart rate and oxygen saturation measured by pulse oxymetry. Conclusions: To study the effects of acupuncture against gagging during TEE, we test the needling of acupoints CV24 and PC6 bilaterally. A placebo acupuncture is used for the control group. Trial registration number: NCT NCT0382142.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".