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Record W2561213378 · doi:10.1093/eurheartj/ehw475

Canadian Cardiovascular Society, the power of collaboration

2016· article· en· W2561213378 on OpenAlexaffabout
Andrew D. Krahn, Heather J. Ross, Anne Ferguson

Bibliographic record

VenueEuropean Heart Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsCanadian Cardiovascular Society
Fundersnot available
KeywordsMedicineCanadian Cardiovascular SocietyPower (physics)Family medicineInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

Working together makes all of us better. The members of the Canadian Cardiovascular Society (CCS) see that when it comes to the health of our patients. More broadly, the success of CCS can be tied to a spirt of collaboration. Canadian Cardiovascular Society traces its roots to 1947. Three Canadian doctors had an idea to create a group of specialized medical experts who could cooperate with and learn from each other. That grew into CCS, Canada’s national voice for cardiovascular clinicians and scientists. Today, CCS has over 2000 members and engages in knowledge translation, professional development and health policy advocacy. To promote cardiovascular health and care excellence, CCS understands—as its founders recognized—that collaboration is critical. That’s part of the national character. Canada has a small population of 35 million spread over almost 10 million Km2 (just slightly smaller than Europe). The country has a national health mandate, but delivery is through 10 individual provinces and three territories.

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)0.009
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.953
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0120.006
Scholarly communication0.0120.004
Open science0.0030.008
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0620.010

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 head0.088
GPT teacher head0.371
Teacher spread0.283 · 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 designNot applicable
Domainnot available
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 routes2
Has abstractyes

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