Histopathology of Polyps and Its Clinical Correlation in Sample of Iraqi Patients Undergoing Colonoscopic Examination
Bibliographic record
Abstract
BACKGROUND /AIMS: Colorectal cancer is the third most common cancer in 2018, the objective of our study was to describe the types and patterns of colorectal polyps in patients presenting to a tertiary care referral center in Baghdad. We also assessed the polyp detection rate (PDR) and adenoma detection rate (ADR). PATIENTS & METHODS: This is single-institution, descriptive cross-sectional study of consenting 103 patients who had colonoscopy done at the Endoscopy Unit of Baghdad teaching hospital, IRAQ from the 1st of June 2018 to 31st of March 2019 after taking verbal consent, The data collected included: Age, sex, Family history of colorectal malignancies and indication for the current colonoscopy. RESULTS: One thousand and thirty patients were included in the study with a mean age of 44 years (SD = 16), with 560 males representing 54.4% and 470 females representing 45.66%.The polyp detection rate in colonoscopies was 19.4% and the adenoma detection rate was 13.6%. Polyps were found and removed in 200 patients, 40% of the removed polyps were tubular adenomas, tubulovillous adenomas in 20%, villous adenomas in 10%, hyperplastic polyps in 5%. The majority of the polyps were in the distal colon in 80% of patients with polyps. CONCLUSIONS: The polyp detection rate was (19.4%) and adenoma detection rate was (13.6%). The majority of polyps were detected in distal colon.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".