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Record W4287372057 · doi:10.5281/zenodo.5636322

Description of rare genetic variants for long QT syndrome identified from a population-based study

2021· article· en· W4287372057 on OpenAlexaff
Gary Tse, Sharen Lee, Tong Liu, Keith Sai Kit Leung, Teddy Tai Loy Lee, Ian Chi Kei Wong, Chloe Miu Mak, Ngai Shing Mok, Kamalan Jeevaratnam, Shuk Han Cheng

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsLong QT syndromeGeneticsGenetic variantsComputational biologyMedicineBiologyInternal medicineQT intervalGenotypeGene

Abstract

fetched live from OpenAlex

Preprint "Description of rare genetic variants for long QT syndrome identified from a population-based study"\n\n \n\nDisclaimer: These datasets were generated during the course of academic research conducted at the Faculty of Medicine, The Chinese University of Hong Kong, which received ethics approval by The Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee of the Hospital Authority. They have been anonymised and are deposited for further advancement of medical research in full compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit\n\n \n\nAccess is permitted for research purposes only.\n\n \n\nThe use of these datasets should provide acknowledgements of such efforts by citing this DOI.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.000

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 head0.069
GPT teacher head0.249
Teacher spread0.180 · 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 teacher head, not a consensus.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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