Impact of WeChat guidance on bowel preparation for colonoscopy: a quasi-experiment study
Bibliographic record
Abstract
Colonoscopy is a standard procedure for screening, monitoring, and treating colorectal lesions. To explore the impact of WeChat guidance on bowel preparation before colonoscopy. This quasi-experiment study included patients who underwent colonoscopy at Qingdao Endoscopy Center between March 2016 and September 2016. The primary outcome was bowel preparation quality (Ottawa score), the secondary outcomes were intubation time, withdrawal time, adenoma detection rate (ADR), and adverse reactions. Finally, 588 patients were included and divided into the WeChat guide (n = 295) and the non-WeChat guide (n = 293) groups, they were comparable in baseline characteristics. The Ottawa score (1.59 ± 1.07 vs. 6.62 ± 3.07, P < 0.001), intubation time (6.47 ± 1.81 vs. 11.61 ± 3.34, P < 0.001), withdrawal time (13.15 ± 3.93 vs. 14.99 ± 6.77, P < 0.001), and occurrence rate of adverse reactions (2.0% vs. 5.5%, P = 0.029) were significantly lower in the WeChat guide group than those in the non-WeChat guide group. ADR was significantly higher in the WeChat guide than that in the non-WeChat guide group (1.47 ± 2.30 vs. 0.84 ± 1.66, P < 0.001). WeChat guidance might improve the quality of bowel preparation and adenoma detection rate, shorten the time of colonoscopy, and reduce adverse reactions in bowel preparation.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".