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Record W3117054467 · doi:10.1097/txd.0000000000001093

A Program of Research to Evaluate the Impact of Deceased Organ Donation Legislative Reform in Nova Scotia: The LEADDR Program

2020· article· en· W3117054467 on OpenAlexaffabout
Matthew J. Weiss, Kristina Krmpotic, Tim Cyr, Sonny Dhanani, Mélanie Dieudé, Jade Dirk, David Hartell, Cynthia Isenor, Lee James, Amanda Lucas, Chelsea Patriquin, Christy Simpson, Victoria L. Sullivan, Karthik Tennankore, Jennifer Thurlow, Robin Urquhart, Hans Vorster, Stephen Beed

Bibliographic record

VenueTransplantation Direct · 2020
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsMcMaster UniversityCanadian Blood ServicesChildren's Hospital of Eastern OntarioTranslational Research in OncologyNova Scotia Department of Health and WellnessNova Scotia Health AuthorityDalhousie UniversityUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsNova scotiaOrgan donationLegislationMedicineJurisdictionLegislatureDonationPopulationFamily medicineEnvironmental healthPolitical scienceLawGeographyTransplantation

Abstract

fetched live from OpenAlex

BACKGROUND: This is the first time deemed consent, where the entire population of a jurisdiction is considered to have consented for donation unless they have registered otherwise, will be implemented in North America. While relatively common in other regions of the world-notably Western Europe-it is uncertain how this practice will influence deceased donation practices and attitudes in Canada. METHODS: We describe a Health Canada funded program of research that will evaluate the implementation process and full impact of the deceased organ donation legislation and the health system transformation in Nova Scotia that includes opt-out consent. RESULTS: There is a need to evaluate the impact of these changes to inform not only Nova Scotia and Atlantic Canada, but also other provincial, national, and international stakeholders. CONCLUSIONS: We establish a rigorous academic framework that we will use to evaluate this significant health system transformation.

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)0.067
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.993

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.005
Scholarly communication0.0050.002
Open science0.0040.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.104
GPT teacher head0.440
Teacher spread0.337 · 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 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

Citations13
Published2020
Admission routes2
Has abstractyes

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