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Record W2975366483 · doi:10.1016/s2589-7500(19)30127-x

An awakening in medicine: the partnership of humanity and intelligent machines

2019· article· en· W2975366483 on OpenAlexaffabout
Leo Anthony Celi, Benjamin Fine, David J. Stone

Bibliographic record

VenueThe Lancet Digital Health · 2019
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsUniversity of TorontoTrillium Health Centre
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsHumanityGeneral partnershipRest (music)The InternetPsychologyInternet privacyMedical educationMedicineComputer scienceWorld Wide WebPolitical scienceLaw

Abstract

fetched live from OpenAlex

In concurrence with the introduction of the internet, widely networked computers, and the collection of large amounts of digital data, the medical profession as a whole has become more self-aware and self-critical. It is increasingly apparent that suboptimal decisions are made at times and, on other occasions, are fatally flawed. Most clinical decisions rest largely on what is referred to as the art of medicine: that is, decision-making that is based on inconsistent and incomplete provider knowledge; variable skills, training, and experience; and last but not the least, an array of biases.

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)0.027
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.027
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0060.066
Scholarly communication0.0240.034
Open science0.0030.015
Research integrity0.0140.020
Insufficient payload (model declined to judge)0.0110.003

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 head0.068
GPT teacher head0.377
Teacher spread0.309 · 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 designTheoretical or conceptual
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

Citations50
Published2019
Admission routes2
Has abstractyes

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