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Record W4366993150 · doi:10.1093/ageing/afad057

Why should clinical practitioners ask about their patients’ concerns about falling?

2023· article· en· W4366993150 on OpenAlexaff
Toby J. Ellmers, Ellen Freiberger, Klaus Hauer, David B. Hogan, Lisa McGarrigle, Mae Ling Lim, Chris Todd, Finbarr C. Martin, Kim Delbaere, Ruud sadly

Bibliographic record

VenueAge and Ageing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Calgary
FundersNational Health and Medical Research CouncilMedical Research CouncilUniversiteit MaastrichtNational Institute for Health and Care ResearchWellcome TrustUniversity of ManchesterDepartment of Health and Social Care
KeywordsMedicineFalling (accident)Ask pricePsychiatry

Abstract

fetched live from OpenAlex

Concerns (or 'fears') about falling (CaF) are common in older adults. As part of the 'World Falls Guidelines Working Group on Concerns about Falling', we recommended that clinicians working in falls prevention services should regularly assess CaF. Here, we expand upon these recommendations and argue that CaF can be both 'adaptive' and 'maladaptive' with respect to falls risk. On the one hand, high CaF can lead to overly cautious or hypervigilant behaviours that increase the risk of falling, and may also cause undue activity restriction ('maladaptive CaF'). But concerns can also encourage individuals to make appropriate modifications to their behaviour to maximise safety ('adaptive CaF'). We discuss this paradox and argue that high CaF-irrespective of whether 'adaptive' or 'maladaptive'-should be considered an indication that 'something is not right', and that is represents an opportunity for clinical engagement. We also highlight how CaF can be maladaptive in terms of inappropriately high confidence about one's balance. We present different routes for clinical intervention based on the types of concerns disclosed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.341
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.418
Teacher spread0.322 · 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 teacher head, 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

Citations44
Published2023
Admission routes1
Has abstractyes

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