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Record W1598667723 · doi:10.5750/ejpch.v2i1.704

Adaptive many model medicine trumps monocultural models: comment on the Miles and Mezzich emergent model of modern clinical practice

2014· article· en· W1598667723 on OpenAlexaff
Robin Nunn

Bibliographic record

VenueEuropean Journal for Person Centered Healthcare · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEpistemologyDominance (genetics)PsychologySociologyPhilosophy

Abstract

fetched live from OpenAlex

In their provocative and insightful discussion paper, Miles and Mezzich consider two parallel, but philosophically divergent movements in medicine: evidence-based medicine and patient-centered care. They call for the integration or coalescence of these contrasting movements into one model that "combines the strengths of both movements, but which dispenses with the weaknesses of each." I share their goal of placing the person at the center of medicine, rather than subordinating the person to the depersonalized science and technology represented by current models of evidence-based medicine. Yet I envision a person-centered model, indeed any medical model, not as an overriding unified entity, but rather as one component in a complex "many model medicine". I have tried to show elsewhere that the use of many models is likely to produce better outcomes than the dominance of any single model. Multiple models entail multiple perspectives and methods that may be necessary to solve difficult medical problems. This pluralistic view is consistent with Peabody's view, cited in the discussion paper, that medical art and science are not opposites, but are foundational components of medicine.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.432
GPT teacher head0.387
Teacher spread0.046 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2014
Admission routes1
Has abstractyes

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