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Record W4394178999 · doi:10.6084/m9.figshare.17982326

Additional file 1 of A personalized biomedical risk assessment infographic for people who smoke with COPD: a qualitative study

2022· dataset· en· W4394178999 on OpenAlexaff
Samir Gupta, Puru Panchal, Mohsen Sadatsafavi, Parisa Ghanouni, Don D. Sin, Smita Pakhalé, Teresa To, Zafar Zafarí, Laura Nimmon

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British ColumbiaHospital for Sick ChildrenOttawa HospitalMcMaster UniversitySickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsInfographicCOPDComputer scienceMedicineData miningInternal medicine

Abstract

fetched live from OpenAlex

Additional file 1: Serial changes to infographic in rapid-cycle design process. John Doe infographic is shown for simplicity; a similar Jane Doe infographic was also assessed. (A) 1-page infographic used in focus group 1, user comments and corresponding changes made. (B) 2-page infographic used in focus group 2, user comments and corresponding changes made. (C) 2-page infographic used in focus group 3, user comments and corresponding changes made. (D) 2-page infographic used in focus group 4, user comments and corresponding changes made.

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)0.004
metaresearch head score (Gemma)0.040
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: Qualitative · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.613
Threshold uncertainty score0.552

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.6130.086

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 head0.039
GPT teacher head0.376
Teacher spread0.337 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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