Age Is the Only Predictor of Poor Bowel Preparation in the Hospitalized Patient
Bibliographic record
Abstract
We examine the impact of key variables on the likelihood of inpatient poor bowel preparation for colonoscopy. Records of inpatients that underwent colonoscopy at our institution between January 2010 and December 2011 were retrospectively extracted. Univariable and multivariable logistic regression models were fitted to assess the effect of clinical variables on the odds of poor preparation. Tested predictors included age; gender; use of narcotics; heavy medication burden; comorbidities; history of previous abdominal surgery; neurological disorder; product used for bowel preparation, whether or not the bowel regimen was given as split or standard dose; and time of endoscopy. Overall, 244 patients were assessed including 83 (34.0%, 95% CI: 28.1-39.9%) with poor bowel preparation. Cecal intubation was achieved in 81.1% of patients (95% CI: 76.2-86.0%). When stratified by quality of bowel preparation, cecal intubation was achieved in only 65.9% (95% CI: 60.0-71.9%) of patients with poor bowel preparation and 89.9% (95% CI: 86.1-93.7%) of patient with good bowel preparation. In multivariate logistic regression analysis, only advancing age was an independent predictor of poor bowel preparation (OR = 1.026, CI: 1.006 to 1.045, and p = 0.008). Age is the only independent predictor of poor bowel preparation amongst hospitalized patients.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".