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Record W4312155534 · doi:10.1017/s0266462322001684

PP23 Lost In Translation? The Differences In The Use Of Real-World Evidence Across Key Markets

2022· article· en· W4312155534 on OpenAlexaboutno aff
Christina-Jane Crossman-Barnes, Weiwei Xu, Ishneet Kaur

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHealth technologyReal world evidenceEconomic growthHealth careEconomics

Abstract

fetched live from OpenAlex

Introduction Health Technology Assessment (HTA) agencies have recognized the importance of real-world evidence (RWE) to inform access decision-making and different HTA agencies establish distinct requirements for their local jurisdictions. The objective of this study is to understand the differences of RWE included in HTA reports and HTA agencies’ perception of RWE. Methods HTA reports from agencies in France, Germany, Spain, Italy, United Kingdom (UK), Canada, Australia and South Korea from January 2011 to November 2021, including original submissions, resubmissions, extensions of original indications and renewals were analyzed. Results Across the eight countries, RWE has been used in nineteen percent of all HTA reports (N=2,960/15,561), with an exponential increase observed between 2019 and 2021. RWE on clinical effectiveness was mostly used in HTA submissions in the UK (twenty-two percent), with twenty-six percent perceived with full acceptance. In contrast, RWE on safety and epidemiology was reported widely in HTA reports in France and Germany (83% and 87%), respectively. Ninety-three percent of RWE received full acceptance in France, followed by forty-four percent in Germany. A mixed picture of the types of RWE included in HTA reports was observed in the other countries, with high variance of acceptance (between 5 to 37%). Conclusions France, Germany, and the UK are the top three countries with a large proportion of HTA reports where RWE was mentioned. The type of RWE used is related to a large extent to the local evidence requirements. For example, RWE around epidemiology was included widely in Germany due to the needs of providing local data for budget impact analyses required by the Federal Joint Committee (G-BA); RWE on tolerability as reported in periodic safety update reports (PSURs) needs to be included in French HTA submissions. RWE on clinical effectiveness has been evaluated the most by the UK HTA bodies.

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)0.300
metaresearch head score (Gemma)0.657
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.300
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3000.657
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0110.014
Science and technology studies0.0020.011
Scholarly communication0.0200.019
Open science0.0030.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0150.003

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 head0.432
GPT teacher head0.515
Teacher spread0.084 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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