Improved high-quality colon cleansing with 1L NER1006 versus 2L polyethylene glycol + ascorbate or oral sulfate solution
Bibliographic record
Abstract
BACKGROUND & AIMS: Colonoscopy requires bowel cleansing for gut mucosa visualization; high-quality cleansing facilitates lesion detection. NER1006 is a 1L polyethylene glycol (PEG) bowel preparation. This post hoc analysis of two randomized trials investigated cleansing efficacy assessed, as in clinical practice, by site endoscopists. METHODS: Patients received NER1006, 2L PEG + ascorbate (2LPEG), or oral sulfate solution (OSS) as a 2-day evening/morning regimen (N2D) or NER1006 morning-only dosing (N1D). Treatment-blinded site endoscopists assessed cleansing using the Harefield Cleansing Scale (HCS). Analyses were conducted in a modified full analysis set, including (mFAS; n = 1378) or excluding (mFAS2; n = 1319) imputed failures, and in patients with 100% treatment adherence (mFAS100; n = 1047). Overall cleansing success (HCS grade A/B), overall high-quality cleansing (HCS grade A), and high-quality segments (HCS 3-4) per treatment population were analyzed. RESULTS: Overall cleansing success was higher with N2D than 2LPEG (92.7-97.5% vs. 87.9-93.0%), and more patients had overall high-quality cleansing with N2D and N1D than 2LPEG (68.0-72.1% and 64.0-68.4% vs. 50.7-56.0%). Without imputed failures, N2D delivered more overall high-quality cleansing than OSS (74.5-77.3% vs. 67.8-69.8%). More high-quality segments were demonstrated with N2D and N1D versus 2 LPEG (82.5-87.1% and 79.4-84.4% vs. 70.4-76.3%) and with N2D versus OSS (82.7-89.5% vs. 78.1-84.4%). CONCLUSION: When assessed by site endoscopists, NER1006 delivers greater high-quality cleansing than 2LPEG or OSS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".