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Record W2113833918 · doi:10.3109/09638288.2011.553706

Measuring elderly dysphagic patients' performance in eating – a review

2011· review· en· W2113833918 on OpenAlexaboutno aff
Tina Hansen, Annette Kjærsgaard, Jens Faber

Bibliographic record

VenueDisability and Rehabilitation · 2011
Typereview
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
FundersHerlev HospitalHospital Research Foundation
KeywordsReliability (semiconductor)PsychologyPsychometricsClinical psychologyInclusion (mineral)Inter-rater reliabilityRating scaleDevelopmental psychology

Abstract

fetched live from OpenAlex

PURPOSE: This review aims to identify psychometrically robust assessment tools suitable for measuring elderly dysphagic patients' performance in eating for use in clinical practice and research. METHOD: Electronic databases, related citations and references were searched to identify assessment tools integrating the complexity of the eating process. Papers were selected according to criteria defined a priori. Data were extracted regarding characteristics of the assessment tools and the evidence of reliability, validity and responsiveness. Quality appraisal was undertaken using developed criteria concerning the study design, the statistics used for the psychometric evaluation and the reported values. RESULTS: Eight of fourteen identified assessment tools met the inclusion criteria. Three assessment tools were specific to dementia, two were specific to stroke and three targeted a range of neurological and geriatric conditions. The rigor of the assessment tools' psychometric properties varied from no evidence available to excellent evidence. Only two assessment tools were rated adequate to excellent. CONCLUSION: 'The Minimal Eating Observation Form-Version II' to be used for screening and 'The McGill Ingestive Skills Assessment' to be used for treatment planning and monitoring appeared to be psychometrically robust for clinical practice and research. However, further research on their psychometric properties is needed.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.095
GPT teacher head0.409
Teacher spread0.313 · 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.

Study designNot applicable
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

Citations38
Published2011
Admission routes1
Has abstractyes

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