High-flow oxygen therapy — its application in COVID-19-related respiratory failure and beyond
Bibliographic record
Abstract
Oxygen therapy is the primary method of treating acute respiratory failure during Sars-CoV-2 infection. Depending on the patient’s condition, treatment may be carried out using traditional nasal cannulas, oxygen masks, non-invasive ventilation or mechanical ventilation. A relatively modern method that has been used worldwide for about 10 years is High Flow Nasal Oxygen Therapy (HFNOT). Equipment for HFNOT allows you to obtain high (up to 60 L/min) flows in nasal cannulas and precisely set a high concentration of oxygen in the mixture of inhaled gases. Such high flow is also associated with the generation of constant positive pressure in the airways, which further supports the treatment of respiratory failure by maintaining airway patency, recruitment of alveoli and reducing the breathing workload. HFNOT also leads to a reduction in anatomical dead space and facilitates carbon dioxide washout from the upper respiratory tract which also reduces the work of breathing and increases the efficiency of ventilation. Moreover, this ventilation method is tolerated well by patients and does not require specialized and longterm personnel training. Therefore, the method was widely applied in hospital wards treating patients with severe respiratory failure during Coronavirus Disease 2019 (COVID-19). Additional applications for this relatively novel method of oxygen support in different fields of medicine were analysed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".