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Record W7036506632

Bewegen en vallen: De kwaliteit van het alledaags lopen als voorspeller van vallen bij ouderen

2016· article· nl· W7036506632 on OpenAlexaff

Bibliographic record

VenueDigital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) · 2016
Typearticle
Languagenl
FieldMedicine
TopicLegal Cases and Commentary
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsQuality of Life ResearchMetropolitan areaChain (unit)
DOInot available

Abstract

fetched live from OpenAlex

Vallen bij ouderen is een groot maatschappelijk probleem. Jaarlijks valt ongeveereen derde van de 65-plussers, en één op de zes personen in deze leeftijdsgroepvalt twee of meer keren per jaar. Een val kan ernstige gevolgen hebben, zoalsbotbreuken, mobiliteitsbeperkingen of bewegingsangst. Om vallen te voorkomenzijn objectieve screeningsinstrumenten noodzakelijk waarmee het valrisicobij ouderen kan worden bepaald. Hier presenteren wij een studie waarin weonderzochten of via draagbare bewegingsmonitoren valrisico daadwerkelijk kanworden voorspeld.

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)0.005
metaresearch head score (Gemma)0.022
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: none
Teacher disagreement score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.018
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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