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Record W2610592127 · doi:10.1503/cmaj.160627

The new medical model: a renewed challenge for biomedicine

2017· article· en· W2610592127 on OpenAlexafffundvenue
Jonathan Fuller

Bibliographic record

VenueCanadian Medical Association Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsThe Wilson Centre
FundersCanadian Institutes of Health Research
KeywordsBiomedicineNarrativePersonalized medicineAlternative medicinePrecision medicineComputer scienceHealth careData scienceMedicineBioinformaticsPolitical scienceArtLiteraturePathology

Abstract

fetched live from OpenAlex

Over the past 25 years, several new “medicines” have come screeching onto health care’s various platforms, including narrative medicine, personalized medicine, precision medicine and person-centred medicine. Philosopher Miriam Solomon calls the first three of these movements different “ways

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)0.048
metaresearch head score (Gemma)0.035
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: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.048
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.003
Science and technology studies0.0100.063
Scholarly communication0.0230.048
Open science0.0040.012
Research integrity0.0170.035
Insufficient payload (model declined to judge)0.0150.003

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 head0.459
GPT teacher head0.544
Teacher spread0.085 · 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

Citations112
Published2017
Admission routes3
Has abstractyes

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