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Record W4388814201 · doi:10.1017/s0714980823000685

Pre-Clinical Mobility Limitation (PCML) Outcomes in Rehabilitation Interventions for Middle-Aged and Older Adults: A Scoping Review

2023· review· en· W4388814201 on OpenAlexafffund
Aiping Lai, Ashley Morgan, Julie Richardson, Lauren E. Griffith, Ayse Kuspinar, Jenna Smith‐Turchyn

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2023
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsImpactMcMaster University
FundersMcMaster University
KeywordsPsychological interventionRehabilitationGerontologyIntervention (counseling)Inclusion (mineral)MedicineDiversity (politics)Physical medicine and rehabilitationPsychologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Individuals with pre-clinical mobility limitation (PCML) are at a high risk of future functional loss and progression to disability. The purpose of this scoping review was to provide a comprehensive understanding of PCML intervention studies in middle-aged and older adults. We present the interventions that have been tested or planned, describe how they have been conducted and reported, identify the knowledge gaps in current literature, and make recommendations about future research directions. An initial search of 2,291 articles resulted in 14 articles that met criteria for inclusion. Findings reveal that: (1) there is limited published work on PCML interventions, especially in middle-aged populations; and (2) the complexity and variety of PCML measures make it difficult to compare findings across PCML studies. Despite the diversity of measures, this review provides preliminary evidence that rehabilitation interventions on PCML help to delay or prevent disability progression.

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)0.009
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.079
GPT teacher head0.371
Teacher spread0.293 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissement→Same topicCerebral Palsy and Movement Disorders→French-language works237,207→