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Record W4401631537 · doi:10.1044/2024_ajslp-22-00183

Participant Characteristics for Dysphagia Research: A Proposed Checklist

2024· review· en· W4401631537 on OpenAlexaff
Sophia Werden Abrams, Atsuko Kurosu, Ashwini Namasivayam‐MacDonald

Bibliographic record

VenueAmerican Journal of Speech-Language Pathology · 2024
Typereview
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChecklistDysphagiaPsychologyMedicineApplied psychologyCognitive psychologySurgery

Abstract

fetched live from OpenAlex

PURPOSE: Participant characteristics are underreported; however, they impact swallowing impairments and subsequent access to assessment and intervention. Standards for rigorous and transparent reporting of dysphagia research are required. The Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS) offers a critical appraisal tool for dysphagia research. This article outlines questions for participant characteristics in dysphagia research as part of the larger FRONTIERS tool. METHOD: An exploratory literature review was conducted to determine how participant characteristics, eligibility criteria, and definitions of health and dysphagia are reported in the literature. Findings were cross-referenced with other relevant critical appraisal tools. A list of questions was generated and refined iteratively with the entire FRONTIERS collaborative until consensus was met. RESULTS: The participant characteristics portion of the FRONTIERS tool includes eight questions and 16 possible subquestions. Examples for how the tool might be used, as well as rationales for inclusion of all questions, are included. CONCLUSIONS: Including detailed characteristics of research participants may support understanding of how best to serve marginalized and underrepresented populations more effectively. Critical appraisal tools, such as FRONTIERS, may help to improve the rigor and transparency in dysphagia research, ultimately improving patient care.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.258
GPT teacher head0.569
Teacher spread0.311 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Speech-Language PathologySame topicDysphagia Assessment and ManagementFrench-language works237,207