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Record W2169079505 · doi:10.1017/s0266462309090199

Health technology assessment: A comprehensive framework for evidence-based recommendations in Ontario

2009· review· en· W2169079505 on OpenAlexaffabout
Ana Johnson, Nancy Sikich, Gerald A. Evans, William K. Evans, Mita Giacomini, Murray Glendining, Murray Krahn, Les Levin, Paul Oh, Charmaine Perera

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2009
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsToronto General HospitalHamilton Health SciencesUniversity of TorontoQueen's UniversityMcMaster UniversityKingston General Hospital
Fundersnot available
KeywordsHealth technologyAdvisory committeeConsistency (knowledge bases)Decision analysisProcess (computing)Technology assessmentMedicineManagement scienceProcess managementPsychologyBusinessHealth carePolitical scienceComputer scienceEngineeringPublic administrationEconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: This study describes the development of a framework for health technology decisions, for Ontario Health Technology Advisory Committee (OHTAC) in Ontario, Canada. METHODS: OHTAC convened a "Decision Determinants Sub-Committee" in January 2007, which undertook a systematic literature review and conducted key informant interviews to develop an explicit decision-making framework. RESULTS: The "Decision Determinants Sub-Committee" offered recommendations about decision criteria, and the process by which decisions are made. Decision criteria include (i) overall clinical benefit, (ii) consistency with societal and ethical values, (iii) value for money, and (iv) feasibility of adoption into the health system. The decision process should be transparent and fair and should use a deliberative process in delivering recommendations. CONCLUSIONS: This methodology is currently being pilot tested in a live environment: OHTAC. It will be evaluated and revised according to its feasibility, acceptability, and perceived usefulness.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0090.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.004
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 head0.479
GPT teacher head0.582
Teacher spread0.103 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2009
Admission routes2
Has abstractyes

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