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Record W4285315819 · doi:10.2196/35724

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

2022· review· en· W4285315819 on OpenAlexvenueno aff
Carina Nina Vorisek, Moritz Lehne, Sophie Anne Inès Klopfenstein, Paula Josephine Mayer, Alexander Bartschke, Thomas Haese, Sylvia Thun

Bibliographic record

VenueJMIR Medical Informatics · 2022
Typereview
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsInteroperabilitySystematic reviewStandardizationHealth careComputer scienceMEDLINEData scienceHealth informaticsMedicineWorld Wide WebPublic healthKnowledge managementNursingPolitical science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.056
metaresearch head score (Gemma)0.200
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.056
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.200
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0120.014
Bibliometrics0.0320.029
Science and technology studies0.0010.002
Scholarly communication0.0050.009
Open science0.0040.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.428
GPT teacher head0.601
Teacher spread0.172 · 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 designSystematic review
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

Citations233
Published2022
Admission routes1
Has abstractyes

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