Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".