When Endoscopic Sedation is Not an Option: Insights From a Multicenter on‐site Survey on Tolerance for Japanese Gastric Cancer Screening
Bibliographic record
Abstract
ABSTRACT Objectives Esophagogastroduodenoscopy (EGD) is widely used for gastric cancer (GC) screening in Japan; however, sedation during EGD is not recommended. We aimed to assess the tolerability of unsedated EGD (USEGD) in the Japanese population. Methods Participants who underwent GC screening in Japanese urban areas between July 2021 and December 2022 were included in this study. We conducted a real‐time questionnaire survey on USEGD invasiveness in 16 clinics and hospitals. Participants completed a self‐report questionnaire, including a six‐point face scale, immediately after undergoing USEGD with an ultrathin endoscope for GC screening, and were placed into the tolerable (T group) or intolerable groups (I group) based on the scores 1–3 and 4–6 on the face scale, respectively. Results The 1021 participants (median age, 59 years; interquartile range, 59–74 years) consisted of 561 men and 437 women, while 23 preferred not to answer. Of the 777 participants who underwent USEGD using an ultrathin endoscope, 135 (17.4%) were categorized into the intolerable (I) group based on severe distress, and 642 (82.6%) into the tolerable (T) group. Multiple logistic regression analysis revealed that older age (odds ratio [OR]: 0.974; p = 0.008) and prior USEGD experience (OR: 0.527; p = 0.006) were associated with higher tolerability. Conversely, females (OR: 2.498; p < 0.001) and first‐time EGD experience (OR: 2.202; p = 0.003) were associated with lower tolerability. Conclusions USEGD was generally well‐tolerated; however, some participants found it intolerable, even with transnasal endoscopy. Supportive measures for these individuals are essential for sustaining effective screening programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".