Optimizing Respiratory-Swallowing Coordination in Patients With Oropharyngeal Head and Neck Cancer
Bibliographic record
Abstract
Swallowing impairment (dysphagia) represents the highest functional morbidity in oropharyngeal (OP) head and neck (HNC) treated either with surgical approaches followed by radiation or with more recent organ preservation protocols, including combined chemotherapy and radiation. Despite the promising overall increasing survival rates, swallowing impairments remain chronic, are often resistant to traditional swallowing therapy, and have devastating consequences on health and well-being. The respiratory-swallow cross-system approach presented here extends beyond traditional swallowing interventions that commonly targets muscles and structures alone, and is instead, directed toward the re-establishment of optimal respiratory-swallowing coordination. Results from our work investigating a respiratory-swallow treatment (RST) paradigm is presented, including results from an RST clinical trial in HNC patients, primarily with OP cancers, with chronic and with intractable dysphagia post-cancer and post-traditional swallowing treatment. Future work will investigate the impact of RST on the degree and durability of clinical outcomes, including oral intake and quality of life, while also examining the potential added benefits of a home practice program that uses a commercially available and easy to use recording and analysis hardware and software.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".