Gesundheitsausgaben – Ermittlung vermeidbarer akutmedizinischer Kosten in einer kostenintensiven Patientenpopulation
Bibliographic record
Abstract
de Oliveira C et al. Determining preventable acute care spending among high-cost patients in a single-payer public health care system. Eur J Health Econ 2019; 20: 869–878 Die Gesundheitsausgaben steigen stetig an und es zeigt sich, dass ein kleiner Anteil der Patienten für einen großen Anteil der Ausgaben verantwortlich ist. Hier fallen vor allem Kosten für Krankenhausaufenthalte und Notfallversorgung ins Gewicht, von denen ein erheblicher Teil jedoch vermieden werden könnte. De Oliveira und Kollegen untersuchen in einer bevölkerungsbezogenen Querschnittsstudie die Höhe und den Anteil der Kosten für vermeidbare Akutmedizin einer kostenintensiven Patientenpopulation in Ontario, Kanada.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".