Comparison of Right Colon Flat Polyp Detection Rate by Water Exchange Versus Water Immersion-pooled Results of Data File of 3 Published Reports
Bibliographic record
Abstract
GOALS: We tested the hypothesis that water exchange (WE) achieved a significantly higher right colon flat polyp detection rate (rFPDR) than water immersion (WI). BACKGROUND: Current endoscopy methods provide real-time morphology but not histopathology. Flat serrated polyps are difficult to find during colonoscopy. In 2022 2 studies reported that the serrated polyp detection rate (SPDR) significantly inversely predicted the development of interval cancers. In 2021 1 systemic review with meta-analysis showed that WE, but not WI increased SPDR. The relative contributions of WE and WI on rFPDR are unknown. STUDY: Individual patient data from 3 reports comparing air insufflation, WI, and WE were pooled. Multiple logistic regression analysis was used to assess the factors associated with a higher rFPDR. RESULTS: The pooled data showed that the rFPDR of air insufflation, WI, and WE were 15.4%, 14.1%, and 19.4% ( P =0.009), respectively. After adjusting for age and withdrawal time, multiple logistic regression analysis revealed that WE, when compared with WI, was significantly associated with a higher rFPDR (adjusted odds ratio[aOR]=1.53, P =0.002). Analysis of data on pathology and size were omitted to avoid duplicating our earlier publications. CONCLUSIONS: Significantly higher rFPDR was achieved by WE. Water exchange rather than WI merits consideration for use to maximize rFPDR. Removal of flat polyps, and by inference serrated polyps, ensures their optimal management to minimize the occurrence of interval cancers. The potential benefit of WE in maximizing SPDR and minimizing interval cancers deserves evaluation in long-term randomized controlled studies focused on flat polyps detection.
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.027 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".