MétaCan
Menu
Back to cohort
Record W2967626462 · doi:10.1016/j.jalz.2019.05.008

Cost‐effectiveness of a collaborative dementia care management—Results of a cluster‐randomized controlled trial

2019· article· en· W2967626462 on OpenAlexafffund
Bernhard Michalowsky, Feng Xie, Tilly Eichler, Johannes Hertel, Anika Kaczynski, Ingo Kilimann, Stefan Teipel, Diana Wucherer, Ina Zwingmann, Jochen René Thyrian, Wolfgang Hoffmann

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcMaster UniversityImpact
FundersDeutsches Zentrum für Neurodegenerative ErkrankungenKempe FoundationDeutsche ForschungsgemeinschaftMcMaster University
KeywordsMedicineRandomized controlled trialDementiaQuality-adjusted life yearQuality of life (healthcare)Cluster randomised controlled trialHealth careCluster (spacecraft)Cost effectivenessResource useCost–utility analysisWillingness to payCost–benefit analysisPhysical therapySurgeryNursingInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The purpose of this study was to determine the cost-effectiveness of collaborative dementia care management (DCM). METHODS: The cost-effectiveness analysis was based on the data of 444 patients of a cluster-randomized, controlled trial, conceptualized to evaluate a collaborative DCM that aimed to optimize treatment and care in dementia. Health-care resource use, costs, quality-adjusted life years (QALYs), and incremental cost per QALY gained were measured over a 24-month time horizon. RESULTS: DCM increased QALYs (+0.05) and decreased costs (-569€) due to a lower hospitalization and a delayed institutionalization (7 months) compared with usual care. The probability of DCM being cost-effective was 88% at willingness-to-pay thresholds of 40,000€ per QALY gained and higher in patients living alone compared to those not living alone (96% vs. 26%). DISCUSSION: DCM is likely to be a cost-effective strategy in treating dementia and thus beneficial for public health-care payers and patients, especially for those living alone.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.322
Teacher spread0.305 · 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.

Study designRandomized trial
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

Citations94
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueAlzheimer s & DementiaSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207