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Record W2995457562 · doi:10.1016/j.clnesp.2019.11.007

European Academy for medicine of ageing session participants' report on malnutrition assessment and diagnostic methods; an international survey

2019· article· en· W2995457562 on OpenAlexaff
Dolores Sánchez‐Rodríguez, Cédric Annweiler, Ester Marco, S Hope, Karolina Piotrowicz, Murielle Surquin, A.H. Ranhoff, Nele Van Den Noortgate, Karen Andersen‐Ranberg, Sylvie Bonin‐Guillaume, Simon Conroy, Adam Gordon, Tomasz Grodziki, Francesco Landi, Nicolás Martínez‐Velilla, Thomas Münzer, Anette Hylen Ranhoff, Regina Roller‐Wirnsberger, Katrin Singler, Hanadi Khamis Al Hamad, Jean‐Baptiste Beuscart, Frédéric Blanc, Annette Ciurea, Katrien Cobbaert, Dhayana Dallmeier, Pascale Dinan, Andreas Engvig, Anette Hansen Højmann, Helka Hosia, Hanna Kerminen, Anne‐Brita Knapskog, Anastasia Koutsouri, Marie Laurent, Matthieu Lilamand, Sophie Marien, Marte Rognstad Mellingsæter, Aline Mendes, Sylvain Nguyen, Chile Ogugua, Nina Ommundsen, Samuel Périvier, Susanna Rapo-Pylkkö, Hanna‐Maria Roitto, Claire Roubaud‐Baudron, Bülent Saka, Francisco Tarazona, Miguel Toscano-Rico, Gaudenz Tschurr, Natalie Vande Walle, Davide Liborio Vetrano, Burcu-Balam Yavuz

Bibliographic record

VenueClinical Nutrition ESPEN · 2019
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsWestern University
FundersEli Lilly and Company
KeywordsMedicineMalnutritionClinical nutritionGeriatricsGerontologyWeight lossBody mass indexClinical PracticeFamily medicinePediatricsObesityInternal medicinePsychiatry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.394
GPT teacher head0.618
Teacher spread0.225 · 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 designObservational
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

Citations28
Published2019
Admission routes1
Has abstractno

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