High‐flow nasal cannula oxygen therapy in acute hypoxemic respiratory failure in 22 dogs requiring oxygen support escalation
Bibliographic record
Abstract
Abstract Objective To determine the effect of high‐flow nasal cannula (HFNC) oxygen therapy on cardiorespiratory variables and outcome in dogs with acute hypoxemic respiratory failure. Design Prospective, sequential clinical trial. Setting University veterinary teaching hospital. Animals Twenty‐two client‐owned dogs that failed to respond to traditional oxygen support. Interventions Initiation of HFNC therapy after traditional oxygen supplementation failed to increase Spo2 > 96% and Pao2 > 75 mm Hg or improve respiratory rate/effort. Measurements and Main Results Physiological variables, blood gas analyses, and dyspnea/sedation/tolerance scores were collected prior to HFNC initiation (on traditional oxygen support [time 0 or T0]), and subsequently during HFNC oxygen administration at time 30 minutes, 60 minutes, and 7 ± 1 hours. Relative to T0, use of HFNC resulted in a decreased respiratory rate at 1 hour (P = 0.022) and 7 hours (P = 0.012), a decrease in dyspnea score at all times (P < 0.01), and an increase in Spo2 at all times (P < 0.01). There was no difference in arterial/venous Pco2 relative to T0, although Paco2 was correlated with flow rate. Based on respiratory assessment, 60% of dogs responded to HFNC use by 30 minutes, and 45% ultimately responded to HFNC use and survived. No clinical air‐leak syndromes were observed. Conclusions HFNC use improved oxygenation and work of breathing relative to traditional oxygen therapies, without impairing ventilation. HFNC use appears to be a beneficial oxygen support modality to bridge the gap between standard oxygen supplementation and mechanical ventilation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".