Optimizing bowel preparation for colonoscopy: A cross-sectional study of the Chinese population
Bibliographic record
Abstract
Background The quality of bowel preparation is an important factor in the success of colonoscopy. However, the quality of bowel preparation is often affected by multiple factors. The main objective of this study was to explore the specific factors that affect the quality of bowel preparation. Methods Patients were consecutively recruited from the gastroenterology department in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology in Wuhan from May 2018 to December 2018. All patients were undergoing colonoscopy. Bowel preparation was evaluated by the Ottawa Bowel preparation Scale (OBPS) and all patients were categorized into 2 groups according to the OBPS. Multivariate analysis was conducted to identify the factors associated with bowel preparation quality. Results A total of 910 patients were included in the analysis with an average age of 48.62 ± 13.57 years. Patient source (P < 0.001) and the preparation method (P = 0.029) were correlated with OBPS adequacy. In addition, after stratified by age, preparation method (P = 0.022) was a significant factor among patients under 50 years old; whereas waiting time (P = 0.005) was a significant factor among patients over 50 years old. Conclusion Bowel preparation should be tailored based on the age of the patients to determine the most appropriate plan, including the most appropriate waiting time and the most appropriate purgative combination. Doctors should also focus more on the quality of bowel preparation in inpatients, who are more likely than outpatients to have an inadequate bowel preparation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".