Evaluating pancreatitis in primary care: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Pancreatitis is an important condition with significant mortality. Primary care may have an important role to play in its prevention, early diagnosis, and ongoing management. AIM: To evaluate incidence, case fatality, and clinical features of acute and chronic pancreatitis in a large population. DESIGN AND SETTING: Population-based cohort study using a primary care database in the UK from 1990 to 2013. METHOD: Use of general practice records from 16 491 patients diagnosed with pancreatitis. Age-standardised incidence rates and case fatality were estimated. Clinical features, aetiology, and patterns of recurrence were evaluated. RESULTS: Incidence of pancreatitis increased from 14.8 in 100 000 (1990-1994) to 31.2 in 100 000 (2010-2013) in males, and from 14.5 to 28.3 in 100 000 in females (2010-2013). Overall case fatality after diagnosis was 4.3% (95% CI = 4.0% to 4.6%) at 90 days and 7.9% (95% CI = 7.5% to 8.4%) at 365 days. In 1990-1994, 10% of patients with acute pancreatitis were recorded as heavy drinkers, increasing to 12% in 2010-2012; for patients with chronic pancreatitis the proportions were 13%, rising to 21%. Among patients who died in the 90 days after diagnosis, 92% consulted with their general practice in the 2 months before first diagnosis. CONCLUSION: The incidence of pancreatitis is increasing over time. Alcohol abuse may now account for at least one in eight cases of acute, and one in five cases of chronic pancreatitis. Consultations among those who subsequently died may have offered potential for earlier diagnosis and intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".