Endoscopic scoring indices for assessing disease severity in familial adenomatous polyposis: Systematic review
Bibliographic record
Abstract
Abstract Background and study aims There is limited consensus on the optimal method for measuring disease severity in familial adenomatous polyposis (FAP). We aimed to systematically review the operating properties of existing endoscopic severity indices for FAP. Methods We searched MEDLINE, EMBASE, and the Cochrane Library from inception to February 2023 to identify randomized controlled trials (RCTs) that utilized endoscopic outcomes or studies that evaluated the operating properties of endoscopic disease severity indices in FAP. Results A total of 134 studies were included. We evaluated scoring indices and component items of scoring indices, such as polyp count, polyp size, and histology. Partial validation was observed for polyp count and size. The most commonly reported scoring index was the Spigelman classification system, which was used for assessing the severity of duodenal involvement. A single study reported almost perfect interobserver and intra-observer agreement for this system. The InSIGHT polyposis staging system, which was used for assessing colorectal polyp burden, has been partially validated. It showed substantial interobserver reliability; however, the intra-observer reliability was not assessed. Novel criteria for high-risk gastric polyps have been developed and assessed for interobserver reliability. However, these criteria showed a poor level of agreement. Other scoring indices assessing the anal transition zone, duodenal, and colorectal polyps have not undergone validation. Conclusions There are no fully validated endoscopic disease severity indices for FAP. Development and validation of a reliable and responsive endoscopic disease severity instrument will be informative for clinical care and RCTs of pharmacological therapies for FAP.
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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".