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Record W2024346606 · doi:10.1016/j.ijcard.2013.10.069

Cardiac resynchronisation therapy optimisation strategies: Systematic classification, detailed analysis, minimum standards and a roadmap for development and testing

2013· review· en· W2024346606 on OpenAlexaff
S.M. Afzal Sohaib, Zachary I. Whinnett, Kenneth A. Ellenbogen, Christoph Stellbrink, T. Alexander Quinn, Margot D. Bogaard, Pierre Bordachar, Berry M. van Gelder, Irene E. van Geldorp, Cecilia Linde, Mathias Meine, Frits W. Prinzen, Robert G. Turcott, Henry M. Spotnitz, Dan Wichterle, Dárrel P. Francis

Bibliographic record

VenueInternational Journal of Cardiology · 2013
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsDalhousie University
FundersNational Institutes of HealthImperial College Healthcare NHS TrustImperial College LondonBritish Heart FoundationNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
KeywordsMedicineScrutinyToolboxProtocol (science)Medical physicsTransparency (behavior)ConcordanceRisk analysis (engineering)Management scienceComputer sciencePathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.027
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.064
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.012
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.141
GPT teacher head0.414
Teacher spread0.273 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations36
Published2013
Admission routes1
Has abstractno

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