Swallowing and Communication Management of Tracheostomy and Laryngectomy in the Context of COVID-19
Bibliographic record
Abstract
IMPORTANCE: The care of patients with a surgically modified airway, such as tracheostomy or laryngectomy, represents a challenge for speech-language pathologists (SLPs) in the context of the coronavirus disease 2019 (COVID-19) pandemic. The objective was to review available publications and practice guidelines on management of tracheostomy and laryngectomy in the context of COVID-19. This study performed a review and synthesis of information available in the PubMed database and from national SLP organizations across 6 countries. OBSERVATIONS: From the search, 22 publications on tracheostomy and 3 referring to laryngectomy were identified. After analysis of titles and abstracts followed by full-text review, 4 publications were identified as presenting guidelines for specific approaches to tracheostomy and were selected; all 3 publications on laryngectomy were selected. The main guidelines on tracheostomy described considerations during management (eg, cuff manipulation, suctioning, valve placement) owing to the increased risk of aerosol generation and transmission during swallowing and communication interventions in this population. Regarding laryngectomy, the guidelines focused on the care and protection of both the professional and the patient, offering recommendations on the management of adverse events and leakage of the tracheoesophageal prosthesis. CONCLUSIONS AND RELEVANCE: Frequent guideline updates for SLPs are necessary to inform best practice and ensure patient and health care worker protection and safety while providing high-quality care and rehabilitation.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".