Speech-Language Pathology Management for Adults With COVID-19 in the Acute Hospital Setting: Initial Recommendations to Guide Clinical Practice
Bibliographic record
Abstract
Purpose This document outlines initial recommendations for speech-language pathology management of adult patients with COVID-19 in the acute hospital setting. Method The authors initially developed these recommendations by adapting those developed for physical therapists working with patients with COVID-19 by Thomas et al. (2020). The recommendations then underwent review by 14 speech-language pathologists and rehabilitation-focused academics representing seven countries (Belgium, Brazil, Canada, Ireland, Japan, New Zealand, the United States). The authors consolidated and reviewed the feedback in order to decide what should be included or modified. Applicability to a global audience was intended throughout the document. Results The authors had 100% agreement on the elements of the recommendations that needed to be changed/modified or added. The final document includes recommendations for speech-language pathology workforce planning and preparation, caseload management, service delivery and documentation, as well as recommendations for the selection of appropriate personal protective equipment and augmentative and alternative communication equipment in the acute care hospital setting. Conclusions Speech-language pathologists play a critical role in the assessment, management, and treatment of patients with COVID-19. Several important considerations need to be made in order to meet the needs of this unique patient population. As more is learned about the impact of the virus on swallowing and communication, the role of the speech-language pathologist on interdisciplinary care teams will remain paramount.
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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 | 0.018 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".