Identifying risk factors for progression to critical care admission and death among individuals with acute pancreatitis: a record linkage analysis of Scottish healthcare databases
Bibliographic record
Abstract
OBJECTIVES: Acute pancreatitis (AP) can initiate systemic complications that require support in critical care (CC). Our objective was to use the unified national health record to define the epidemiology of AP in Scotland, with a specific focus on deterministic and prognostic factors for CC admission in AP. SETTING: Health boards in Scotland (n=4). PARTICIPANTS: We included all individuals in a retrospective observational cohort with at least one episode of AP (ICD10 code K85) occurring in Scotland from 1 April 2009 to 31 March 2012. 3340 individuals were coded as AP. METHODS: Data from 16 sources, spanning general practice, community prescribing, Accident and Emergency attendances, hospital in-patient, CC and mortality registries, were linked by a unique patient identifier in a national safe haven. Logistic regression and gamma models were used to define independent predictive factors for severe AP (sAP) requiring CC admission or leading to death. RESULTS: 2053 individuals (61.5% (95% CI 59.8% to 63.2%)) met the definition for true AP (tAP). 368 patients (17.9% of tAP (95% CI 16.2% to 19.6%)) were admitted to CC. Predictors of sAP were pre-existing angina or hypertension, hypocalcaemia and age 30-39 years, if type 2 diabetes mellitus was present. The risk of sAP was lower in patients with multiple previous episodes of AP. In-hospital mortality in tAP was 5.0% (95% CI 4.1% to 5.9%) overall and 21.7% (95% CI 19.9% to 23.5%) in those with tAP necessitating CC admission. CONCLUSIONS: National record-linkage analysis of routinely collected data constitutes a powerful resource to model CC admission and prognosticate death during AP. Mortality in patients with AP who require CC admission remains high.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".