Comparative performance and external validation of three different scores in predicting inadequate bowel preparation among Greek inpatients undergoing colonoscopy.
Bibliographic record
Abstract
Background: Predictive scores aim to predict bowel preparation adequacy among hospitalized patients undergoing colonoscopy. We evaluated the comparative efficacy of these scores in predicting inadequate bowel cleansing in a cohort of Greek inpatients. Methods: analysis of data generated from a cohort of inpatients undergoing colonoscopy in 4 tertiary Greek centers to validate the 3 models currently available (models A, B and C). We used the Akaike information criterion to quantify the performance of each model, while Harrell's C-index, as the area under the receiver operating characteristics curve (AUC), verified the discriminative ability to predict inadequate bowel prep. Primary endpoint was the comparison of performance among models for predicting inadequate bowel cleansing. Results: Overall, 261 patients-121 (46.4%) female, 100 (38.3%) bedridden, mean age 70.7±15.4 years-were included in the analysis. Model B showed the highest performance (Harrell's C-index: AUC 77.2% vs. 72.6% and 57.5%, compared to models A and C, respectively). It also achieved higher performance for the subgroup of mobilized inpatients (Harrell's C-index: AUC 72.21% vs. 64.97% and 59.66%, compared to models A and C, respectively). Model B also performed better in predicting patients with incomplete colonoscopy due to inadequate bowel preparation (Harrell's C-index: AUC 74.23% vs. 69.07% and 52.76%, compared to models A and C, respectively). Conclusions: Predictive model B outperforms its comparators in the prediction of inpatients with inadequate bowel preparation. This model is particularly advantageous when used to evaluate mobilized inpatients.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".