Effect of narrow-banding imaging on detection rate of ascending colon polyps in patients with different intestinal cleanliness
Bibliographic record
Abstract
Objective To evaluate the influence of narrow-banding imaging on the detection rate of polyps in the ascending colon in patients with different intestinal cleanliness. Methods A retrospective study was performed on 1029 patients who underwent colonoscopy at the Endoscopy Center of the No. 1 People′s Hospital of Suqian from January 2016 to January 2017. These cases were divided into two groups by intestinal cleanliness score: A and B. Group A had an Ottawa intestinal cleanliness score of 0 or 1 (n = 549), and group B had an intestinal cleanliness score of 2 (n = 480). In group A, 260 patients underwent NBI to detect ascending colon polyps (subgroup A1), and 289 underwent conventional imaging (subgroup A2). Similarly, 231 patients in group B received NBI (subgroup B1), and 249 patients underwent conventional imaging (subgroup B2). The detection rate of ascending colon polyps and examination time were compared between subgroups A1 and A2 and subgroups B1 and B2. Results The detection rate of ascending colon polyps and examination time were 6.92% (18/260) and (127.93±12.21) seconds in subgroup A1, 3.11% (9 /289) and (126.17±11.32) seconds in subgroup A2, 5.19% (12/231) and (125.45±15.16) seconds in subgroup B1, and 4.42% (11/249) and (128.88±8.26) seconds in group B2, respectively. The detection rate of ascending colon polyps was significantly higher in subgroup A1 than in subgroup A2 (P 0. 05). Conclusion Narrow-banding imaging can significantly increase the detection rate of ascending colon polyps in the case of intestinal cleanliness score of 0 or 1, but not in patients with an intestinal cleanliness score of 2. Key words: Narrow-banding imaging; Ascending colon; Polyp detection rate
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 | 0.001 |
| 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 | 0.000 |
| 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".