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Record W2032323376 · doi:10.1353/pbm.2006.0012

Redefining Disease? The Nosologic Implications of Molecular Genetic Knowledge

2006· article· en· W2032323376 on OpenAlexaff
Fiona A. Miller, Megan E. Begbie, Mita Giacomini, Catherine Ahern, Erin A. Harvey

Bibliographic record

VenuePerspectives in biology and medicine · 2006
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDiseaseCategorizationTypologyInterpretation (philosophy)NosologyIdentification (biology)PsychologyEpistemologyMedicineBiologySociologyComputer sciencePathologyPsychiatry

Abstract

fetched live from OpenAlex

How will developments in genetic knowledge affect the classification of disease? Leaders in genetics have suggested that knowledge of the role of genes in disease can determine nosology. Diseases might be defined by genotype, thus avoiding the limitations of more empirical approaches to categorization. Other commentators caution against disease definitions that are detached from the look and feel of disease, and argue for an interplay between genotypic and phenotypic information. Still others attribute nosologic change to social processes. We draw on an analysis of the scientific literature, our conversations with genetics clinicians, and reviews of patient organization Web sites to offer a revised interpretation of the nosologic implications of molecular genetic knowledge. We review the recent histories of three diseases--hemophilia, Rett syndrome, and cystic fibrosis--to argue that nosologic change cannot be explained by either biologic theories of disease etiology or sociologic theories of social tendencies. Although new genetic information challenges disease classifications and is highly influential in their redesign, genetic information can be used in diverse ways to reconstruct disease categories and is not the only influence in these revisions. Ironically, genetic information is likely to play a central role in producing a new, but still empirical, classification scheme.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.378
Teacher spread0.340 · 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 designTheoretical or conceptual
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

Citations25
Published2006
Admission routes1
Has abstractyes

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