Herr Tur-Tur und die Krankenhausvergleiche: Ein Besuch in der Psychiatrie-Oase
Bibliographic record
Abstract
Ziel der Studie: Verweildauern werden gegenwärtig verstärkt als Indikator für die Effektivität der stationären Behandlungen in Krankenhäusern von Kostenträgern gefordert. Krankenhausvergleiche anhand dieses Indikators werden speziell in der Psychiatrie zumeist unter Hinweis auf heterogene Patientenzusammensetzungen abgelehnt. Die Studie vergleicht acht psychiatrische Krankenhäuser unter Berücksichtigung von individuellen Patientenmerkmalen und von organisatorischen Unterschieden. Methodik: Datenbasis: 27792 Patientendokumentationen nach DGPPN-Bado aus acht Krankenhäusern mit 135 Stationen. Statistische Analyse: hierarchische lineare Modelle mit Random-Koeffizienten zur Bestimmung der Varianzkomponenten für drei Ebenen (Patienten, Stationen, Krankenhäuser). Ergebnisse: Die betrachteten acht Krankenhäuser zeigten nach Berücksichtigung von Patienteneinflüssen und Stationsgliederung keinerlei überzufällige Variation in der Verweildauer. Schlussfolgerung: Krankenhausvergleiche sind methodisch korrekt nur unter Berücksichtigung aller relevanter Datenebenen möglich. Qualitätsvergleiche und nachfolgende Maßnahmen in der stationären Psychiatrie sollten nicht auf Krankenhausebene, sondern auf Stationsebene erfolgen. Comparative Hospital Tours: Visiting the Oasis of Psychiatry Aim: Currently, financial sponsors and Government officials of the relevant ministries responsible for the German health care system request comparative length-of-stay figures in hospitals as indicators of efficacy of inpatient treatment. In psychiatry such comparisons are considered doubtful because of the heterogeneity of patients in different hospitals. This study compares the length of stay in eight German psychiatric hospitals accounting for individual as well as organisational characteristics. Methods: Sample: 27,792 patient records according to the DGPPN-BADO (the standardized psychiatric assessment and discharge battery in Germany) from eight hospitals with a total of 135 wards. Statistical Analysis: Variance components modelling with random effects using three levels (patients, wards, hospitals). Results: After adjustment for patient and organisational characteristics, there were no significant differences in the average length of stay between the eight hospitals. Conclusions: Hospital comparisons of quality of care require a multilevel approach. Based on our results further comparisons and implementation of quality improvements in inpatient psychiatry should focus on the ward level.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".