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Record W2980912419 · doi:10.12927/hcpol.2019.25942

Creating Clinical Cohorts: Challenges Encountered in Two Canadian Provinces

2019· article· en· W2980912419 on OpenAlexaffvenueabout
Esther S. Shoemaker, Clare Liddy, Leigh M. McClarty, Shabnam Asghari, Jillian Hurd, Sean B. Rourke, Souradet Y. Shaw, Christine Bibeau, Ron Rosenes, Philip Lundrigan, Lois M. Crowe, Laurie Ireland, Carla Loeppky, Claire Kendall

Bibliographic record

VenueHealthcare policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsNine Circles Community Health CentreGovernment of ManitobaSt. Michael's HospitalUniversity of ManitobaBruyèreMemorial University of NewfoundlandManitoba Health
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Linkage (software)Health carePublic healthData collectionHealthcare systemPolitical scienceEconomic growthPublic relationsMedicineFamily medicineNursingSociology

Abstract

fetched live from OpenAlex

In 2013, the Living with HIV (LHIV) Innovation team established clinical cohorts of people living with HIV in Manitoba and Newfoundland and Labrador, and they linked the data to provincial health administrative databases. Access to these data enabled researchers to conduct studies across provincial borders; contribute to a national dialogue on HIV health system performance; and give recommendations for evidence-based healthcare, health policy and public health. However, research funding is episodic; maintaining cohorts requires stable funding. We support the establishment of a cross-jurisdictional approach to facilitate streamlined data collection and linkage without interruption and to allow for meaningful analysis in order to inform national policies.

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)0.066
metaresearch head score (Gemma)0.106
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 score0.820
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.106
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.010
Science and technology studies0.0360.005
Scholarly communication0.0120.003
Open science0.0080.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.077
GPT teacher head0.464
Teacher spread0.386 · 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

Citations8
Published2019
Admission routes3
Has abstractyes

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