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Record W2147699671 · doi:10.5712/rbmfc10(35)1069

Multimorbidity and Quaternary Prevention (P4)

2015· article· en· W2147699671 on OpenAlexaff
Dee Mangin, Iona Heath

Bibliographic record

VenueRevista Brasileira de Medicina de Família e Comunidade · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPolypharmacyMultimorbidityMedicineDrug reactionDiseasePrimary careComorbidityChronic diseaseNorm (philosophy)GerontologyIntensive care medicinePsychiatryFamily medicineDrugInternal medicine

Abstract

fetched live from OpenAlex

Multimorbidity has become the norm for the majority of patients attending primary care, and while the proportion of those with multimorbidity is higher in older age, the absolute number of people with multimorbidity is greater in those under 65. The specialist-based single-disease model of treatment assumes that each index disease is the dominant illness within the complex system and that the other comorbid illnesses are held constant while management is focussed on the single condition. Thus, applying single disease guidelines to a person with five chronic comorbidities, no matter what they are, results in potentially harmful polypharmacy. This approach has led to the current ‘epidemic’ in morbidity and mortality from adverse drug reactions that now outstrip the target diseases as a cause of death. In this article, we highlight four characteristics of quaternary prevention framework that policymakers should take into account when considering the quality of health care.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.093
GPT teacher head0.366
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2015
Admission routes1
Has abstractyes

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