ASSESSMENT OF SEVERITY OF ULCERATIVE COLITIS ON FIRST COLONOSCOPIC EXAMINATION
Bibliographic record
Abstract
Objective: To assess the severity of ulcerative colitis on first colonoscopic examination. Study Design: Prospective cross-sectional (correlational) study design. Place and Duration of Study: Study was conducted in Gastroenterology Outpatient Department of Pak Emirates Military Hospital, Rawalpindi, from Nov 2017 to Oct 2018. Methodology: An aggregate of 200 patients within the age range of 12-70 years, were included in the studythrough non-probability consecutive sampling. The data was collected by the self-administered questionnaireincluding age, gender, stool frequency, P/R bleed, systemic features of ulcerative colitis & colonoscopic findings.Effectiveness of the procedures was noted on a pre-designed performa and the endoscopic assessment was based upon mayo score severity of colitis graded from Normal (0) to Severe (3). Data was analyzed by using SPSS-19. Results: The mean age of the participants was reported 38 ± 2.1 years. Out of 200 participants 104 (52%) weremale, diarrhea with PR bleed was positive in 180 (90%) & anemia in 154 (77%). Colonoscopic findings showedthat 72 (36%) were with Left sided colitis (Montreal Class E2) & 82 (41%) with proctitis (Montreal class E1). Severe disease (Mayo endoscopic Score 3) was positive in 118 (59%) patients. Conclusion: Assessment of severity of UC is important as it determines the long term management & alsovaluable for risk stratification to predict the prognosis. Our findings feature the requirement for system levelenhancements to encourage the proper delivery of colonoscopy services dependent on individual risk. Keywords: , , , .
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".