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Record W2758973470 · doi:10.1111/jep.12831

Reasoning, evidence, and clinical decision‐making: The great debate moves forward

2017· editorial· en· W2758973470 on OpenAlexaff
Michael Loughlin, Robyn Bluhm, Stephen Buetow, Kirstin Borgerson, Jonathan Fuller

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2017
Typeeditorial
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health NetworkDalhousie University
Fundersnot available
KeywordsContext (archaeology)RationalityEpistemologyValue (mathematics)PsychologyEngineering ethicsClinical PracticeEvidence-based medicineHealth careMEDLINEMedicinePolitical scienceLawComputer scienceNursingHistory

Abstract

fetched live from OpenAlex

When the editorial to the first philosophy thematic edition of this journal was published in 2010, critical questioning of underlying assumptions, regarding such crucial issues as clinical decision making, practical reasoning, and the nature of evidence in health care, was still derided by some prominent contributors to the literature on medical practice. Things have changed dramatically. Far from being derided or dismissed as a distraction from practical concerns, the discussion of such fundamental questions, and their implications for matters of practical import, is currently the preoccupation of some of the most influential and insightful contributors to the on-going evidence-based medicine debate. Discussions focus on practical wisdom, evidence, and value and the relationship between rationality and context. In the debate about clinical practice, we are going to have to be more explicit and rigorous in future in developing and defending our views about what is valuable in human life.

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)0.032
metaresearch head score (Gemma)0.103
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.032
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.103
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.002
Science and technology studies0.0040.015
Scholarly communication0.0170.011
Open science0.0030.003
Research integrity0.0140.029
Insufficient payload (model declined to judge)0.0050.003

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 head0.127
GPT teacher head0.560
Teacher spread0.434 · 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
GenreEditorial

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

Citations16
Published2017
Admission routes1
Has abstractyes

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