MétaCan
Menu
Back to cohort
Record W2613843517 · doi:10.22374/cjgim.v12i1.204

Planifier pour l’avenir

2017· article· fr· W2613843517 on OpenAlexvenueno aff
Mitchell Levine

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2017
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Dans le présent numéro de la RCMIG, Quinn et coll. discutent des mérites des médecins qui prennent le temps de se concentrer sur les objectifs globaux du plan de soins chez les patients atteints de démence avancée et qu’ils voient pour un problème aigu. Plutôt que de traiter immédiatement la nouvelle maladie, Quinn et coll. suggèrent que le plan de soins clinique soit envisagé dans le contexte des objectifs et attentes globaux spécifiques à la personne atteinte de démence avancée. Évidemment, à moins que ces derniers ne soient clairement énoncés par avance, c’est un problème de savoir en quoi ils consistent pour un patient qui est en état avancé de déclin cognitif. Dans une telle situation, les fournisseurs de soins de santé doivent souvent s’en remettre à la famille du patient pour avoir des indications, mais celle-ci n’en a pas toujours vraiment.

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)0.012
metaresearch head score (Gemma)0.033
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 score0.018
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0060.007
Open science0.0020.005
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0180.006

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 head0.097
GPT teacher head0.420
Teacher spread0.324 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of General Internal MedicineSame topicHealth, Medicine and SocietyFrench-language works237,207