Knowledge translation tools to guide care of non-intubated patients with acute respiratory illness during the COVID-19 Pandemic
Bibliographic record
Abstract
Providing optimal care to patients with acute respiratory illness while preventing hospital transmission of COVID-19 is of paramount importance during the pandemic; the challenge lies in achieving both goals simultaneously. Controversy exists regarding the role of early intubation versus use of non-invasive respiratory support measures to avoid intubation. This review summarizes available evidence and provides a clinical decision algorithm with risk mitigation techniques to guide clinicians in care of the hypoxemic, non-intubated, patient during the COVID-19 pandemic. Although aerosolization of droplets may occur with aerosol-generating medical procedures (AGMP), including high flow nasal oxygen and non-invasive ventilation, the risk of using these AGMP is outweighed by the benefit in carefully selected patients, particularly if care is taken to mitigate risk of viral transmission. Non-invasive support measures should not be denied for conditions where previously proven effective and may be used even while there is suspicion of COVID-19 infection. Patients with de novo acute respiratory illness with suspected/confirmed COVID-19 may also benefit. These techniques may improve oxygenation sufficiently to allow some patients to avoid intubation; however, patients must be carefully monitored for signs of increased work of breathing. Patients showing signs of clinical deterioration or high work of breathing not alleviated by non-invasive support should proceed promptly to intubation and invasive lung protective ventilation strategy. With adherence to these principles, risk of viral spread can be minimized.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".