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Our genes are not our destiny: incorporating molecular medicine into clinical practice

2008· review· en· W1500176999 on OpenAlexafffund
Stephen J. Genuis

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsUniversity of Alberta
FundersUniversity of Calgary
KeywordsDestiny (ISS module)MedicineClinical PracticeComputational biologyFamily medicineBiologyEngineering

Abstract

fetched live from OpenAlex

In many developed nations, the state of publicly administered health care is increasingly precarious as a result of escalating numbers of chronically ill patients, inadequate medical personnel and hospital facilities, as well as sparse funding for ongoing upgrades to state-of-the-art diagnostic and therapeutic technology - an increased emphasis on aetiology-centred medicine should be considered in order to achieve improved health for patients and populations. Medical practice patterns which are designed to provide quick and effective amelioration of signs and symptoms are frequently not an enduring solution to many health afflictions and chronic disease states. Recent scientific discovery has rendered the drug-oriented algorithmic paradigm commonly found in contemporary evidence-based medicine to be a reductionist approach to clinical practice. Unfolding evidence appears to support a genetic predisposition model of health and illness rather than a fatalistic predestination construct - modifiable epigenetic and environmental factors have enormous potential to influence clinical outcomes. By understanding and applying fundamental clinical principles relating to the emerging fields of molecular medicine, nutrigenomics and human exposure assessment, doctors will be empowered to address causality of affliction when possible and achieve sustained reprieve for many suffering patients.

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)0.026
metaresearch head score (Gemma)0.267
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.267
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.242
GPT teacher head0.565
Teacher spread0.322 · 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
GenreReview

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
Published2008
Admission routes2
Has abstractyes

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