MétaCan
Menu
← Back to cohort
Record W7104386278 · doi:10.5281/zenodo.17553320

Dr. Marjory Warren: The Mother of Geriatrics

2000· article· en· W7104386278 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2000
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsGeriatricsConvictionWork (physics)Accident (philosophy)Older peopleTask (project management)

Abstract

fetched live from OpenAlex

Marjory Warren (1897 - 1960) was a person with innovation and dynamism. Her work was missionary and her proposals visionary. She was a surgeon to start with, and yet she created geriatrics out of medicine. She advocated practising geriatrics as a specialist, and yet she emphasized the importance of generalist training. She argued for having separate wards with environment and caring process appropriate for elderly patients, but she insisted that these wards should be an integral part of a general hospital with equal access to diagnostic and therapeutic facilities. She was thankful that she was not distracted by fame during her early endeavours so that she could quietly build up this special branch of medicine. Her concern for human life, not only for the unwanted and unfittest elderly patients but also for her passengers, ended up in her tragic death in a car accident at the age of 62. Her work continues to influence and inspire those who share her conviction that elderly people deserve the best 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)0.001
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0120.007

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 head0.045
GPT teacher head0.298
Teacher spread0.254 · 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
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

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicAging and Gerontology Research→French-language works237,207→