Proposing a Multisystem Swallowing Framework: A Network Medicine Approach in the Era of COVID-19
Bibliographic record
Abstract
Purpose: Swallowing impairments (dysphagia) following severe COVID-19 are complex, as is recovery from the disease itself. Like other critical illnesses, dysphagia management requires multidisciplinary involvement owing to the interaction between numerous physiological systems. Our objectives are to (a) propose a literature-based network medicine framework highlighting multisystem considerations for dysphagia management following critical illness including COVID-19 and (b) discuss clinician innovation and the evolution of dysphagia practice during a global pandemic. Method: A literature search identified current and relevant studies in areas pertinent to speech-language pathologists caring for patients with COVID-19. Our tutorial presents a network medicine framework of critical illness dysphagia and its “phenotypic” presentation with application to COVID-19. We also consider the individual and collective burden of the illness and global pandemic. Results: Iatrogenic and complex pathophysiologies likely contribute to dysphagia during critical illness. Upper aerodigestive tract functions, specifically swallowing, rely upon multiple systems for safe execution. Critical illness comorbidities, particularly respiratory challenges and supportive ventilation, are features of COVID-19 often exacerbating dysphagia risk. Throughout the pandemic, increased demands on and reallocation of resources have led to clinical adaptations across settings and placed significant burden on those who deliver care. Conclusions: Care provision for patients with COVID-19 relies on dynamic knowledge about disease mechanisms and effective interventions. Dysphagia management should employ a multidisciplinary and multisystem approach. Together, clinicians and health care systems should endeavor to proactively establish robust infrastructure and appropriate funding streams to optimize outcomes when considering the cumulative impacts of COVID-19.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".