Analysing Patient Trajectories of Individuals with Alcohol Use Disorders (PRAGMA)
Bibliographic record
Abstract
Abstract: Aim: Alcohol use is causing a considerable health burden for individuals and society in Germany. To reduce the burden from alcohol use, ensuring optimal treatment for those who are in need, is key. With this data-linkage study, we aim to provide a comprehensive description of healthcare service use among individuals with alcohol use disorders (AUD) in Hamburg, the second-largest German city. Methods: The study population is defined as adults living in Hamburg, currently insured by one of two statutory health insurance funds and with at least one alcohol-specific ICD-10 code between 2016 and 2021. Additionally, we will obtain data from pension funds and the Hamburg basic data monitoring system of outpatient addiction aid. By using unique identifiers, individual register data from these three sources will be linked. Hypotheses and qualitative analyses are presented in the form of research questions to analyse administrative prevalence rates, patient trajectories and predictors of treatment success as well as to estimate the impact of prototypical care pathways and the COVID-19 pandemic on utilization of alcohol-specific healthcare services. Discussion: The study ‘Patient Routes of People with Alcohol Use Disorders in Germany’ (PRAGMA) will be the first to provide an in-depth understanding of treatment provision for people with AUD in Germany. Following up a heterogeneous sample of people with AUD for six years will provide a unique opportunity to compare current with recommended care pathways as well to identify options for care improvements.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".