Clinicopathologic Features of Colorectal Polyps in Shahid Beheshti University of Medical Sciences (SBMU)
Bibliographic record
Abstract
Aim: This study was designed to report epidemiologic findings of polyps in Iranian patients, and predict histology of polyp regarding to demographic and colonoscopic findings. Background: Classification of colorectal polyps had been revised in the past two decades and there is a need for polyp categorization in the Iranian Health System. Patients and methods: In this retrospective study, the medical records of patients with colonoscopic diagnosis of polyp in pathology departments of SBMU affiliated teaching hospitals were reviewed. Patient’s slides evaluated and demographics findings were assessed. The anatomical location, macroscopic appearance including size and histological assessment of all polyps were recorded. Results: From total number of 1106 polyps (detected in 862 patients), adenomatous polyps (638 [57.7%]) were the most prevalent findings, followed by colon mucosal tag (184[16.6%]), hyperplastic and serrated polyps (122[11%]), inflammatory polyps (110[9.9%]), hamartomatous (21[1.9%]), and malignant lesions (13[1.2%]). Multivariate logistic regression showed age (each one year increasing age; odds ratio [OR] = 1.026, 95%confidence interval [CI] = 1.016–1.036, p < 0.0001), location of polyp (right colon; OR = 1.905, 95%CI = 1.366–2.656, p < 0.0001), and polyp size of 5-10 mm (OR = 1.662, 95%CI = 1.214–2.276, p = 0.002), and polyp size of >10 mm (OR = 2.778, 95%CI = 1.750–4.411, p< 0.0001) were independently associated with neoplastic polyps. Also, polyp size of >10 mm (OR= 2.613, 95%CI= 1.083-6.307, p=0.033), tubulovillous pattern of polyp (OR= 3.508, 95%CI= 1.666-7.387, p=0.001) and villous pattern of polyp (OR= 10.444, 95%CI= 4.211-25.905, p<0.0001) were associated with high grade dysplasia in neoplastic polyps. Conclusion: Increased age, location of polyp (right colon), increased size of polyp and villous component of polyp could classify patients in high risk groups.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".