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Record W4206932173 · doi:10.1503/cmaj.210455

The commercialization of patient data in Canada: ethics, privacy and policy

2022· article· en· W4206932173 on OpenAlexafffundvenueabout
Sheryl Spithoff, Jessica Stockdale, Robyn Rowe, Brenda McPhail, Nav Persaud

Bibliographic record

VenueCanadian Medical Association Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsWomen's College HospitalUniversity of TorontoSt. Michael's Hospital
FundersWomen's College Hospital
KeywordsPatient privacyGovernment (linguistics)CommercializationPharmacyData anonymizationPatient dataInformation privacyInternet privacyPrivacy lawInformed consentPrivacy policyMedicineData scienceComputer scienceFamily medicineBusinessAlternative medicineHealth carePolitical scienceMarketing

Abstract

fetched live from OpenAlex

KEY POINTS In Canada, commercial data brokers are currently able to use deidentified patient data from pharmacies, private drug insurers, the federal government and medical clinics without patient consent. They are able to do this because of a lack of privacy protections for deidentified data. A

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)0.014
metaresearch head score (Gemma)0.138
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.138
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
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 head0.226
GPT teacher head0.489
Teacher spread0.263 · 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 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

Citations21
Published2022
Admission routes4
Has abstractyes

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