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Geriatric Medicine: an Evidence-based Approach

2015· book· en· W1576264287 on OpenAlexfundno aff

Bibliographic record

VenueOxford University Press eBooks · 2015
Typebook
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersSchool of MedicineGeriatric Research Education and Clinical CenterDartmouth CollegeYork UniversityMassachusetts General HospitalSt. John's UniversityJohns Hopkins UniversityBrown UniversityUniversity of WashingtonUniversity of California, San DiegoBrigham and Women's HospitalMoffitt Cancer CenterNIH Clinical CenterU.S. Department of Veterans Affairs
KeywordsOlder peopleHealth carePopulation ageingHealth professionalsMedicineGeriatricsDiseaseResource (disambiguation)GerontologyAlternative medicinePopulationClinical trialPsychologyFamily medicinePsychiatryEnvironmental healthPolitical sciencePathology

Abstract

fetched live from OpenAlex

Geriatric medicine: an evidence based approach is an online clinical reference for health care professionals who manage older patients, and summarizes up-to-date research literature in a style that can be directly applied by busy healthcare professionals and provide a useful resource for reference. Because people are living longer and the population over the age of 60 is burgeoning, there are repercussions for health services and healthcare expenditure in developed countries. This online resource covers disease aetiology, diagnosis, and treatment specific to older people, and how they differ from those of the general adult population. Additionally, it covers the complicated co-morbidities older people often have and how they respond to treatment in different ways compared to younger people. It also addresses how evidence of efficacy of different treatments is often lacking because older people are under-represented in clinical trials.

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)0.016
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0140.009
Science and technology studies0.0010.003
Scholarly communication0.0090.010
Open science0.0030.005
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0170.009

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 head0.087
GPT teacher head0.310
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations106
Published2015
Admission routes1
Has abstractyes

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