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Record W2092635158 · doi:10.1002/ppul.21364

Resource usage in outpatient care and reimbursement for cystic fibrosis in Germany

2010· article· en· W2092635158 on OpenAlexaboutno aff
Daniela Eidt‐Koch, Thomas Wagner, Thomas Mittendorf, Andreas Reimann, Johann‐Matthias Graf von der Schulenburg

Bibliographic record

VenuePediatric Pulmonology · 2010
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRemunerationReimbursementActivity-based costingAmbulatory careCystic fibrosisMedical prescriptionEmergency medicineAmbulatoryQuarter (Canadian coin)Intensive care medicinePediatricsSurgeryInternal medicineHealth careNursingFinance

Abstract

fetched live from OpenAlex

OBJECTIVE: Objective of this study is to assess and evaluate resource use in outpatient treatment in Germany and to compare it with remuneration. METHODS: Outpatient treatment was evaluated in seven different centers for pediatric and adult CF patients. Data were recorded during one representative month in 2006. A micro-costing approach was used to value resource use data. RESULTS: For outpatient treatment mean costs (excluding drugs) of 488 € per patient per quarter occurred. Correlation analyses identified significant cost drivers including age and co-morbidities (pancreatic insufficiency, hepatobiliary complications, lung function capacity, or bacterial lung colonization). Remuneration covered only 51% of the total costs (252 € per patient/quarter). CONCLUSIONS: As the human resources available to these centers today are already below the requirements set by the European consensus for standards of CF care it will be important for a high level of patient care to reach a cost-covering remuneration scheme.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.290
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2010
Admission routes1
Has abstractyes

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