Validierung eines Kostenkalkulationsalgorithmus und Identifizierung von Kostentreibern
Bibliographic record
Abstract
Neugeborenen-Intensivstationen (NICUs; Neonatal Intensive Care Units) verursachen mehr als 35 % der pädiatrischen Krankenhauskosten. Ein besseres Verständnis der Ausgaben auf NICUs kann helfen, Bereiche mit Verbesserungspotenzial zu identifizieren. Ziel dieser Studie war es, den Kostenberechnungsalgorithmus des Canadian Neonatal Network (CNN) für 7 Case-Mix-Gruppen mit den tatsächlichen Kosten einer tertiären NICU zu validieren und die Kostentreiber zu untersuchen. Fazit Den Autoren zufolge prognostiziert der CNN-Algorithmus die Gesamtkosten der NICU für 7 Case-Mix-Gruppen. Die Personalkosten machen 3 Viertel der krankenhausinternen Gesamtkosten aller Neugeborenen in der NICU aus. Sehr unreife Frühgeborene, die vor der 33. SSW geboren werden, beanspruchen den größten Teil der Ressourcen der NICU. Publication History Article published online: 25 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.042 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".