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Record W4290928441 · doi:10.1016/j.procs.2022.07.032

Fear of falling and risk factors in older adults

2022· article· en· W4290928441 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversité Laval
FundersEcole Supérieure des Communications de TunisNew York University
KeywordsFear of fallingFalling (accident)SlownessBalance (ability)Elderly peopleFall preventionPhysical medicine and rehabilitationComputer sciencePoison controlInjury preventionMedicineGerontologyMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

Fear of falling is becoming the most common fear among people aged 65 and over,with a prevalence that varies within the range of 12% to 92% [1]. Monitoring the elderly's physical activity is highly recommended in order to improve their health. Several reports have demonstrated that a fall does not always result in a fear of falling, which may be caused by factors other than the fall itself[2][3]. Falls, on the other hand, and fear of falling share one feature: they are both associated with walking disorders. Falls are considered a significant risk that primarily affects the elderly. Slowness of movement, loss of motion, and balance issues are common in the elderly. In this article, we have proposed an intelligent insole system to detect falls, monitor patients’ physiological parameters, and reduce their fear of falling. Thus, the elderly will be surrounded in real time by professional clinicians working remotely to control them during their daily activities.

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.

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 score0.036
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.312
Teacher spread0.295 · 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