Participant Characteristics for Dysphagia Research: A Proposed Checklist
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.316 | 0.460 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".