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Record W4224301868 · doi:10.1136/medethics-2022-108129

Imagination and idealism in the medical sciences of an ageing world

2022· article· en· W4224301868 on OpenAlexaff
Colin Farrelly

Bibliographic record

VenueJournal of Medical Ethics · 2022
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsQueen's University
Fundersnot available
KeywordsIdealismAgeingDiseaseParadigm shiftPopulation ageingMedicineControl (management)PopulationGerontologyPsychologyEpistemologyPhilosophyPathologyComputer scienceInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Imagination and idealism are particularly important creative epistemic virtues for the medical sciences if we hope to improve the health of the world's ageing population. To date, imagination and idealism within the medical sciences have been dominated by a paradigm of disease control, a paradigm which has realised significant, but also limited, success. Disease control proved particularly successful in mitigating the early-life mortality risks from infectious diseases, but it has proved less successful when applied to the chronic diseases of late life (like cancer). The time is ripe for the emergence and prominence of a supplementary medical research paradigm, the paradigm of 'healthy ageing' which prioritises the goal of rate (of ageing) control rather than disease control. This is the difference between extending the human healthspan versus extending survival by managing (or trying to eliminate) the multi-morbidities, frailty and disability currently prevalent in late life. The idealism of the disease control paradigm is myopic because it ignores the health constraints imposed by the inborn ageing process itself, a biological reality which is already inflicting significant economic and disease burdens on the world's ageing populations. Unless the medical sciences retard the rate of biological ageing, these problems will continue to be amplified as larger numbers of persons survive into late life.

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)0.036
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
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 score0.471
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0360.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.104
GPT teacher head0.443
Teacher spread0.339 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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