Endoscopic size measurement of colorectal polyps: a systematic review of techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Accurate size measurement of colorectal polyps is critical for clinical decision making and patient management. This systematic review aimed to evaluate the current techniques used for colonic polyp measurement to improve the reliability of size estimations in routine practice. Methods: A comprehensive literature search was conducted across PubMed, EMBASE, and MEDLINE to identify studies relevant to size measurement techniques published between 1980 and March 2024. The primary outcome was the accuracy of polyp sizing techniques used during colonoscopy. Results: 61 studies were included with 34 focusing on unassisted and assisted endoscopic visual estimation and 27 on computer-based tools. There was significant variability in visual size estimation among endoscopists. The most accurate techniques identified were computer-based systems, such as virtual scale endoscopes (VSE) and artificial intelligence (AI)-based systems. The least accurate techniques were visual or snare-based polyp size estimation. VSE assists endoscopists by providing an adaptive scale for real-time, direct, in vivo polyp measurements, while AI systems offer size measurements independent of the endoscopist’s subjective judgment. Conclusion: This review highlights the need for standardized, accurate, and accessible techniques to optimize sizing accuracy during endoscopic procedures. There is no consensus on a gold standard for measuring polyps during colonoscopy. While biopsy forceps, snare, and graduated devices can improve the accuracy of visual size estimation, their clinical implementation is limited by practical, time, and cost challenges. Computer-based techniques will likely offer improved accuracy of polyp sizing in the near future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it