Evaluation of Factors Contributing to Diagnosis of Crohn’s Disease in the Face of Increasing Trend in Pakistan
Bibliographic record
Abstract
Background: Crohn's disease (CD) is characterized by granulomatous inflammation of the digestive tract. Diagnosing CD involves assessing clinical symptoms, radiological and endoscopic findings, and histopathological evidence. Although previously considered a disease in developed countries, CD is increasing in developing nations, but challenges exist in diagnosing CD promptly. This study aims to report diagnostic parameters for early and correct CD diagnosis in Pakistan. Methodology: A retrospective analysis from June 2016 to August 2023 of 22 CD patients was done, by data from medical records, questionnaires completed at diagnosis, and telephonic interviews. Baseline demographic and clinical characteristics were assessed, and patients were categorized using the Montreal classification. Results: CD was diagnosed in 22 patients, with a 1:1 male-to-female ratio with a mean age of 33 years (range 15-55 years). Symptoms at presentation included abdominal pain (95.5%), watery diarrhea (86.4%), fever (31.8%), rectal bleeding (54.5%), and weight loss (81.8%) with 68% having symptoms for over 12 months before diagnosis. Disease characteristics were diverse, with various patterns of involvement and histopathological findings. Conclusions: In resource-limited countries like Pakistan, the timely diagnosis of CD presents a significant healthcare challenge. Therefore, it is necessary to tackle these complex problems by enhancing diagnostic capabilities, raising medical awareness, and improving access to healthcare resources.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".