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Record W4394394071 · doi:10.6084/m9.figshare.22625122

Additional file 2 of Optimizing communication strategies and designing a comprehensive program to facilitate cascade testing for familial hypercholesterolemia

2023· dataset· en· W4394394071 on OpenAlexaff
Gemme Campbell‐Salome, Laney K. Jones, Nicole Walters, Kelly Morgan, Andrew Brangan, Ilene Ladd, Mary P. McGowan, Katherine Wilemon, Tara Schmidlen, Emilie Simmons, M. Schwartz, Megan McMinn, Eric P. Tricou, Alanna Kulchak Rahm, Catherine D. Ahmed, Amy C. Sturm

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsCascadeComputer scienceEngineering

Abstract

fetched live from OpenAlex

Additional file 2: Supplemental Figure 2a. The original Family Sharing Tool (FST). The original FST with questions and answers for probands. The FST included a separate page to facilitate sharing of the Cascade Chatbot to at-risk relatives. Supplemental Figure 2b. The Family Sharing Chatbot (FSC). The FST was optimized into a FSC to be conversational and interactive chat to encourage probands to share information about their FH result with at-risk relatives.

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)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.448
Threshold uncertainty score0.788

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4480.095

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 head0.155
GPT teacher head0.328
Teacher spread0.174 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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