A Proposed Framework for Rigor and Transparency in Dysphagia Research: Prologue
Bibliographic record
Abstract
PURPOSE: Scientific transparency and rigor are essential for the successful translation of knowledge in clinical research. However, the field of oropharyngeal dysphagia research lacks guidelines for methodological design and reporting, hindering accurate interpretation and replication. This article introduces the Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS), a new critical appraisal tool intended to support optimal study design and results reporting. The purpose of introducing FRONTIERS at this early phase is to invite pilot use of the tool and open commentary. METHODS: FRONTIERS was developed by collaborating researchers and trainees from six international dysphagia research labs. Eight domains were identified, related to study design, swallowing assessment methods, and oropharyngeal dysphagia intervention reporting. Small groups generated questions capturing rigor and transparency for each domain, based on examples from the literature. An iterative consensus process informed the refinement and organization of primary and subquestions, culminating in the current initial version of FRONTIERS. RESULTS: FRONTIERS is a novel tool, intended for use by oropharyngeal dysphagia researchers and research consumers across disciplines. A web application enables provisional use of the tool, and an accompanying survey solicits feedback regarding the framework. CONCLUSION: FRONTIERS seeks to foster rigor and transparency in the design and reporting of oropharyngeal dysphagia research. We encourage provisional use and invite user feedback. A future expert consensus review is planned to incorporate feedback. By promoting scientific rigor and transparency, we hope that FRONTIERS will support evidence-based practice and contribute to improved health outcomes for individuals with oropharyngeal dysphagia.
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 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 | 0.741 | 0.718 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.024 | 0.014 |
| Science and technology studies | 0.015 | 0.066 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".