Accuracy of Manitoba administrative health databases to identify patients with cirrhosis
Bibliographic record
Abstract
Introduction: Cirrhosis is associated with a substantial clinical and economic burden on healthcare systems worldwide. Currently, health system planning for cirrhosis is challenged by a lack of provincial prevalence and incidence information. Administrative health data may be one way to complete surveillance and monitoring of cirrhosis burden in Manitoba; however, an algorithm to identify cases of cirrhosis does not exist. Therefore, the objectives of this thesis, were to develop and validate administrative data algorithms for cirrhosis and describe the epidemiology of cirrhosis in Manitoba overtime and investigate changes in its incidence and prevalence across various age groups and between males and females. Methods: A retrospective study was conducted using linked provincial administrative health data to primary care electronic medical records (EMR) to develop and validate algorithms for identifying cases of cirrhosis. A validated case definition from EMR data was the reference standard. Two optimal algorithms were used to estimate the burden and epidemiology of cirrhosis overtime. Annual incidence rates were estimated using a generalized linear model and generalized estimating equations with a negative binomial distribution adjusting for age and sex. Results: The estimates of sensitivity, specificity, and positive predictive value (PPV) of the two optimal algorithms when compared to a reference standard of validated primary care case definition ranged 42.8%-67.9%, 95.8%-97.5%, 18.4%-19.3% respectively. Application of these algorithms to provincial data demonstrated an increase in age- and sex-adjusted incidence and prevalence rates of cirrhosis between 2010-2019. The incidence of cirrhosis showed an average annual increase of 4-6%, with the most significant increases ranged 6-8% per year (p < 0.0001) in young adults aged 18-44 years. Regardless of the algorithm employed, the incidence rates among females were consistently higher than those among males (p <0.0001). Furthermore, the rate of change in cirrhosis incidence was significantly higher among young adults (p = 0.001) and females (p=.03). Conclusion: Cirrhosis patients can be identified from administrative health data with modest accuracy when a validated case definition for primary care EMRs was the reference standard. The incidence and prevalence of cirrhosis substantially increased overtime, signaling an alarming shift in the disease burden towards younger individuals and females.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".