Evaluation of Endoscopist and Pathologist Factors Affecting the Incidence of Microscopic Colitis
Bibliographic record
Abstract
BACKGROUND: Microscopic colitis (MC) is an umbrella term for collagenous colitis (CC) and lymphocytic colitis (LC). The incidence of these diseases is increasing for unclear reasons. OBJECTIVE: To identify factors that may impact diagnosis rates of MC in a North American population. METHODS: Population-based pathology and endoscopy databases were searched to identify all cases of MC and the number of lower endoscopy (LE) procedures performed over a five-year period (January 2004 to December 2008) in a catchment area of 1.2 million people. Endoscopist characteristics were compared with diagnostic rates. RESULTS: MC incidence increased from 1.68 per 10,000 in 2004, to 2.68 per 10,000 in 2008, with an average annual increase of 12% per year (95% CI 7% to 16%; P<0.0001). The incidence rate of LC increased but the rate of CC remained stable over the study period. Approximately one-half of the cases were probable and one-half were definite based on pathologists' reports - a proportion that remained stable over time. The number of LEs per population increased by 4.6% annually over the study period (95% CI 2.8% to 6.4%; P<0.0001), and biopsy rates in LE for MC indications (eg, unexplained diarrhea, altered bowel habits) increased over time (3.4% annual increase [95% CI 1.8% to 6.0%]; P<0.001). Endoscopists with an academic practice, gastroenterologists and those with lower annual endoscopy volumes were more likely to make a diagnosis of MC. CONCLUSION: The incidence of MC is rising due to increased diagnosis of LC, while CC incidence remains stable. Patients with MC symptoms have stable endoscopy rates but are being biopsied more often. Physician training, practice type and endoscopy volume impact the diagnostic rates of MC.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".