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Record W2480977984 · doi:10.1136/bmj.i4293

Opt-in method is vital for data sharing

2016· letter· en· W2480977984 on OpenAlexaff
Frank Sullivan, Brian McKinstry

Bibliographic record

VenueBMJ · 2016
Typeletter
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsNorth York General HospitalUniversity of Toronto
FundersMedical Research Council
KeywordsAccountabilityComputer scienceCorporate governanceData sharingHealth informaticsData governanceInformaticsOpt-outPublic healthData scienceBusinessMedicineWorld Wide WebPolitical scienceNursingAlternative medicineLawData qualityMarketingFinance

Abstract

fetched live from OpenAlex

We agree with van Staa and colleagues on health data, that “The ultimate solution, however, must combine new technologies with clear accountability, transparent operations, and public trust.”1 One method to achieve the necessary confidence is to create an opt-in mechanism for the use of patient identifiable data.2 Building on the Scottish Health Informatics Programme (SHIP) governance mechanisms and …

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)0.182
metaresearch head score (Gemma)0.427
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.995
Threshold uncertainty score0.960

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1820.427
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0080.027
Scholarly communication0.0110.033
Open science0.0050.014
Research integrity0.0410.081
Insufficient payload (model declined to judge)0.0180.013

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 head0.838
GPT teacher head0.711
Teacher spread0.127 · 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 designNot applicable
DomainReproducibility
GenreCommentary

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
Published2016
Admission routes1
Has abstractyes

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