Speech-Language Pathology Management for Adults with COVID-19 in the Acute Hospital Setting: What Do We Know?
Bibliographic record
Abstract
The purpose of this study was to collate and summarize the content covered in published literature describing speech-language pathology management of adult patients with COVID-19 in the acute hospital setting as of February 2022. This review serves as an updated review of the initial recommendations to guide speech-language pathology management for adults with COVID-19 in the acute hospital setting previously provided by Namasivayam-MacDonald and Riquelme in July of 2020. This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review Extension protocol. We searched for relevant peer-reviewed articles in the following electronic databases: MEDLINE, EMBASE, and CINAHL. The article review process was conducted using Covidence. Our searches yielded a combined total of 3019 unique citations, of which 54 were accepted for full-text review. Thirty-seven of the 54 studies were review articles, recommendations, or opinion pieces. This translates to mostly low levels of evidence (i.e., Levels VI and VII) and a grade of ‘D’ when applying the American Society of Plastic Surgeons grade recommendation scale for evidence-based clinical practice guidelines, meaning there is little or no systematic empirical evidence available. The remaining 17 provided empirical data, which translates to higher levels of evidence and a grade of ‘B’. The empirical data shared in this scoping review provide support for the ongoing role of the SLP in the acute care setting and the impact COVID-19 and its variants have on the underlying systems for communication and swallowing. This document serves as further proof of the need for ongoing research into the clinical presentations of patients with speech-language, cognitive and/or swallowing deficits resulting from COVID-19, as well as into systems of care that will provide the best outcomes in their rehabilitation.
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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 | 0.015 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".