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Record W1998150270 · doi:10.1016/j.eurger.2014.07.010

Living Lab Falls-MACVIA-LR: The falls prevention initiative of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) in Languedoc-Roussillon

2014· article· en· W1998150270 on OpenAlexaff
Hubert Blain, Frédéric Abécassis, P.A. Adnet, B. Alomène, Michel Amouyal, Benoît G. Bardy, M.-P. Battesti, G. Baptista, Pierre Louis Bernard, J. Berthe, C. Boubakri, J. Burille, M.-V. Calmels, Bernard Combe, Didier Delignières, Arnaud Dupeyron, G Dupeyron, Agnès Oude Engberink, F. Gressard, D. Hève, D. Jakovenko, Claude Jeandel, M. Lapierre, M.S. Léglise, Isabelle Laffont, C. Laurent, Béatrice Lognos, J.-M. Lussert, Kévin Mandrick, V. Marmelat, P. Martin-Gousset, A. Matheron, Grégoire Mercier, Cyril Meunier, Jacques Morel, Grégory Ninot, F. Nouvel, J.P. Ortiz, M.-P. Pasdelou, E. Pastor, J. Pélissier, Stéphane Perrey, Marie‐Christine Picot, N. Pinto, Sofiane Ramdani, F. Radier-Pontal, E. Royère, I. Rédini-Martinez, Jean‐Marie Robine, Émile Roux, J.-L. Savy, Yannick Stéphan, D. Strubel, G. Tallon, K. Torre, Jean‐Michel Verdier, Grégoire Vergotte, E. Viollet, Cédric T. Albinet, Joël Ankri, Cédric Annweiler, Athanase Bénétos, Olivier Beauchet, Gilles Berrut, Patricia Dargent‐Molina, Leslie M. Decker, Olivier Hanon, M.E. Joël, F. Nourashemi, François Puisieux, Yves Rolland, Geneviève Ruault, Bruno Vellas, Anne Vuillemin, Christian Becker, N. Holand, J.-P. Michel, Timo Strandberg, Anna Bedbrook, Sophie A. Granier, T. Camuzat, Rodolphe Bourret, Nicolas Best, O. Jonquet, J.-E. de La Coussaye, Jacques Mercier, M. Noguès, M. Aoustin, Philippe Domy, J. Bringer, Philippe Augé, Céline Bourquin, Jean Bousquet

Bibliographic record

VenueEuropean Geriatric Medicine · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMedicineHealthy ageingGeneral partnershipCoachingGerontologyFall preventionLiving labPoison controlSuicide preventionAgeingMedical emergencyManagementFinance

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)0.006
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.004

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 head0.079
GPT teacher head0.383
Teacher spread0.304 · 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

Citations34
Published2014
Admission routes1
Has abstractno

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