Exercise-Based Dysphagia Treatment: A Proposed Checklist
Bibliographic record
Abstract
PURPOSE: Oropharyngeal swallowing exercise-based interventions are frequently utilized to target physiologic mechanisms with the goal of improving swallowing function. However, study replicability and evidence synthesis regarding effects of interventions are limited due to inconsistent reporting on factors known to influence treatment delivery. In order to promote consistency of reporting factors associated with replicability, the authors constructed a set of preferred parameters focused on dysphagia as part of the initial version of the larger tool (Framework for RigOr aNd Transparency In REseaRch on Swallowing or FRONTIERS). METHOD: Thirty-eight initial questions were assembled by the authors as part of the treatment subsection. Questions were then reviewed by individuals in the FRONTIERS collaborative who have expertise in research, clinical practice, or both. RESULT: Twenty-four questions were removed following review, reducing the final set of treatment-focused questions to 14 questions. CONCLUSIONS: The revised set of questions provides users of the exercise-based treatment section of the FRONTIERS checklist with an initial checklist to promote transparency and rigor to improve study replicability and evidence synthesis. We intend for this treatment section of FRONTIERS to undergo further refinement following commentary and feedback.
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 | 0.140 | 0.348 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".