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Record W4312128930 · doi:10.1136/jnis-2022-019849

Mechanical thrombectomy is cost-effective versus medical management alone around Europe in patients with low ASPECTS

2022· article· en· W4312128930 on OpenAlexaboutno aff
Manuel Moreu, Raffaele Scarica, Carlos Pérez-García, Santiago Rosati, Alfonso López‐Frías, J. Egido, C. Gómez–Escalonilla, Patricia Simal, Juan Arrazola, Anne‐Laure Bocquet, Thomas Barthe

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersStryker
KeywordsMedicineQuality-adjusted life yearWillingness to payModified Rankin ScaleStroke (engine)Cost effectivenessQuality of life (healthcare)Cost–benefit analysisDemographyEmergency medicineIschemic strokeNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To demonstrate, by a cost-effectiveness analysis, the efficiency of mechanical thrombectomy (MT) versus medical management (MM) in patients with a low Alberta Stroke Program Early CT Score (ASPECTS) from the RESCUE Study. METHODS: A cost-effectiveness model was designed to project both direct medical costs and quality-adjusted life-years (QALYs) of MT versus MM in eight European countries (Spain, UK, France, Italy, Belgium, Germany, Sweden, and the Netherlands). Our model was created based on previously published health-economic data in those countries. Procedure costs, acute, mid-term, and long-term care costs were projected based on expected modified Rankin Scale (mRS) scores as reported in the RESCUE-Japan LIMIT trial. RESULTS: MT was found to be a cost-effective option in eight different countries across Europe (Spain, Italy, UK, France, Belgium, Germany, the Netherlands, and Sweden). with a lifetime incremental cost-effectiveness ratio varying from US$2 875 to US$11 202/QALY depending on the country. A cost-effectiveness acceptability curve showed 100% acceptability of MT at the willingness to pay (WTP) of US$40 000 for the eight countries. CONCLUSIONS: MT is efficient versus MM alone for patients with low ASPECTS in eight countries across Europe. Patients with a large ischemic core could be treated with MT because it is both clinically beneficial and economically sustainable.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.275
Teacher spread0.254 · 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 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

Citations31
Published2022
Admission routes1
Has abstractyes

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