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Record W4307608179 · doi:10.1101/2022.10.22.22280644

Preclinical mobility limitation outcomes in rehabilitation interventions for middle-aged and older adults population: a scoping review protocol

2022· review· en· W4307608179 on OpenAlexaff
Aiping Lai, Ashley Morgan, Julie Richardson, Lauren E. Griffith, Ayse Kuspinar, Jenna Smith‐Turchyn

Bibliographic record

VenuemedRxiv · 2022
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCINAHLPsycINFOMEDLINEData extractionContext (archaeology)Psychological interventionMedicineInclusion (mineral)Intervention (counseling)PopulationRehabilitationProtocol (science)GerontologyInclusion and exclusion criteriaPsychologyAlternative medicineNursingPhysical therapySocial psychologyEnvironmental healthPathology

Abstract

fetched live from OpenAlex

Abstract Objectives This scoping review aims to understand the extent of evidence regarding preclinical mobility limitation (PCML) intervention studies that have been implemented or planned in middle-aged and older adult populations. Introduction Individuals with PCML are at a high risk of future functional loss and progression to disability. An overview of studies undertaken on this emerging topic is now due. Inclusion criteria Rehabilitation intervention studies that measured PCML outcomes or assessed individuals at the PCML stage will be included. Studies will be considered if the participants are middle-aged (45-64yrs) or older adults (≥ 65yrs) in any setting, including community-dwelling, hospital discharges, or institutional settings. Methods Seven databases (MEDLINE, EMBASE, AMED, PsycINFO, CINAHL, Web of Science and Cochrane CENTRAL) were searched to locate relevant published and unpublished intervention studies (English evidence from inception onwards). The search strategy will be generated using the PCC framework (population, concept, and context) and refined after consulting with a McMaster research librarian. In addition, a manual search from the reference list of retrieved papers and review articles will also be performed. Two reviewers will use predefined inclusion/exclusion criteria to independently review titles, abstracts, and full texts of potential articles. Any disagreements on study selection will be resolved by discussion or consensus involving a third reviewer. Data will be collected and reported using a predefined data extraction chart and described using qualitative content analysis.

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)0.095
metaresearch head score (Gemma)0.076
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.095
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.076
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0140.014
Bibliometrics0.0220.015
Science and technology studies0.0050.005
Scholarly communication0.0090.008
Open science0.0060.008
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0510.011

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 head0.189
GPT teacher head0.477
Teacher spread0.289 · 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
GenreProtocol

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
Published2022
Admission routes1
Has abstractyes

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