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Record W3114496789

The New Medical Model: Chronic Disease and Evidence-based Medicine

2016· dissertation· en· W3114496789 on OpenAlexfundno aff
Jonathan Fuller

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typedissertation
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of California, San DiegoUniversity of Toronto
KeywordsMedicineData scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

In the traditional medical model, physicians cure acute diseases using knowledge from the basic biomedical sciences. With the rise of chronic disease and evidence-based medicine (EBM) in the second half of the Twentieth Century, ‘the new medical model’ emerged. In the new medical model, physicians prevent or manage chronic diseases using the principles of EBM. Chronic diseases and EBM have created a host of daunting problems for modern medicine. In this thesis dissertation, my Central Aim is a philosophical analysis of chronic disease and of EBM, especially concerning treatment and prevention. I also maintain a Central Thesis: many practical problems in chronic disease care and in EBM are intimately connected to conceptual, metaphysical and epistemic problems. The practical problems include: reductionism; fractured care; multifactorialism; the growing burden of chronic diseases; treatment effect heterogeneity, treatment futility and treatment harm; cookbook medicine; the tyranny of aggregate outcomes; RCT worship; unrepresentative trials; and multimorbidity. The philosophical problems I explore are the following. In Chapter 2, I examine the nature of chronic diseases and advance a metaphysical account in which chronic diseases are bodily properties. In Chapter 3, I re-examine existing models of disease classification and argue for a new descriptive model (the ‘constitutive model’) as well as a new guiding principle for disease prevention (‘the monomechanism ideal’). In Chapter 4, I develop a general account of why mechanistic models often fail to predict the results of medical interventions. In Chapter 5, I reconstruct the standard model of prediction in medicine, the ‘Risk Generalization-Particularization (Risk GP) Model’. In Chapter 6, I develop a theory of causal inference in comparative group studies that illuminates the roles of randomization, confounders and causes. Finally, in Chapter 7 I show why EBM’s preferred approach to generalizing trial results, ‘simple extrapolation’, is a deeply problematic solution to the problem of extrapolation. Throughout the ongoing discussion, we indeed find that many practical problems in modern medicine are tangled up with philosophical problems. It will require both medical and philosophical wisdom to unravel them.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.556
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.000

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 head0.069
GPT teacher head0.431
Teacher spread0.362 · 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
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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