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Record W2339275260 · doi:10.36834/cmej.36717

The science of communication, the art of medicine

2016· article· en· W2339275260 on OpenAlexaffvenue
Marcel D’Eon

Bibliographic record

VenueCanadian Medical Education Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

We have all heard people talk (or write) about the art and science of medicine. Sometimes we mean that there’s a certain art or craft to clinical medicine, the application of science to real people in real situations. Sometimes we are referring to the unpredictable human side of medicine, the relationships and rapport building essential to clinical and professional practice. Whether we refer to clinical judgment or communication skills, what we imply is that in science we “know” but in art we are “winging it.” Definitions of science include words such as facts, principles, laws, truth, knowledge, and systematic, while definitions of art include creative skill, imagination, appreciation, and beauty. In Roze des Ordons et al. (this issue, 2016) we read that a misplaced and inappropriate word might leave a more lasting and painful scar than a surgeon’s sloppy scalpel, or as they put it, “… unhelpful communication can cause iatrogenic suffering, with a lasting impact upon patients and families and residual uncertainty and emotional distress amongst trainees, [thus] difficult discussions should be considered as seriously as an invasive procedure.” Our interactions with patients can have lasting and profound consequences so we should not be “winging it.” We need to move the art and craft of communications to a higher level where principles and laws of human behaviour and complex interactions are systematically learned and skillfully applied.

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)0.004
metaresearch head score (Gemma)0.064
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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 designNot applicable
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

Citations8
Published2016
Admission routes2
Has abstractyes

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