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Record W4220881104 · doi:10.1080/14737167.2022.2055549

Kick-off meeting of the TOWWERS showcase project: 1 <sup>st</sup> collaborative value-based healthcare anchored on real-world data involving the 5P+

2022· article· en· W4220881104 on OpenAlexaffabout
Julie Frappier, Marilyn Krelenbaum, Eva García Villalba, Pierre Martin, Nadia Nour, Jacques L’Espérance, Anie Perrault, Barbara Decelle, Anne-Marie Larose, Valérie Viau, R. P. Fahey, Jean-François Denault, Isabelle Fauchon, Maxime Huard, Manon Frappier

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsImpactBristol-Myers Squibb (Canada)
Fundersnot available
KeywordsHealth carePandemicCoronavirus disease 2019 (COVID-19)Healthcare systemValue (mathematics)BusinessPublic relationsPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has highlighted the need to modernize healthcare systems to the reality of the 21st century. The first world-wide Strategic Committee to launch Collaborative Value-Based Healthcare (C-VBHC) anchored on populational Real World Data and structured collaboration, took place in Montreal, via TOWWERS showcase project. The meeting covered a broad range of topics from the perspective of each of the various Real-World healthcare actors, the 5P+: Patient, Prescriber, Producer, Policymaker, Payer, including Data and Research stakeholders. Attended by approximately 20 participants from North America and Europe, the meeting provided a valuable opportunity to unit the 5P+ around common goals and exchanging on solutions. TOWWERS Strategic committee identified key elements required to continue the transformation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.102
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0080.003
Open science0.0020.009
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.1020.021

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 head0.423
GPT teacher head0.612
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2022
Admission routes2
Has abstractyes

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