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Record W3048504392 · doi:10.1016/j.xjtc.2020.08.003

Commentary: Thrombosis and hemorrhage, yin and yang

2020· letter· id· W3048504392 on OpenAlexafffund
Hellmuth R. Muller Moran, Rakesh C. Arora

Bibliographic record

VenueJTCVS Techniques · 2020
Typeletter
Languageid
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsResearch ManitobaSt. Boniface Hospital
FundersPfizer CanadaAbbott NutritionEdwards Lifesciences
KeywordsMedicineThrombosisAdverse effectCardiac surgeryPopulationVenous thrombosisSurgeryArteryAnemiaCardiologyInternal medicine

Abstract

fetched live from OpenAlex

The ancient Chinese yin and yang reflect the duality of universe; the dark yin is antithetical to the light yang, yet one cannot exist without the other. So too it is with thrombosis and hemorrhage in cardiac surgery. Maintaining a balance between these 2 opposed processes is necessary to successfully complete a cardiac operation, whereas too much of one (or not enough of the other) will inevitably lead to adverse events and outcomes. In no other patient population is this balance perhaps more delicate than in those who cannot receive blood products, such as patients who are a practicing Jehovah's Witness.

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)0.003
metaresearch head score (Gemma)0.019
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 score0.067
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0040.004
Open science0.0030.001
Research integrity0.0670.051
Insufficient payload (model declined to judge)0.0100.009

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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
Published2020
Admission routes2
Has abstractyes

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