Variability in low-flow oxygen delivery by nasal cannula evaluated in neonatal and infant airway replicas
Bibliographic record
Abstract
Abstract Background The nasal cannula is considered a trusted and effective means of administering low-flow oxygen and is widely used for neonates and infants requiring oxygen therapy, despite an understanding that oxygen concentrations delivered to patients are variable. Methods In the present study, realistic nasal airway replicas derived from medical scans of children less than 3 months old were used to measure the fraction of oxygen inhaled (FiO2) through nasal cannulas during low-flow oxygen delivery. Parameters influencing variability in FiO2were evaluated, as was the hypothesis that measured FiO2values could be predicted using a simple, flow-weighted calculation that assumes ideal mixing of oxygen with entrained room air. Tidal breathing through neonatal and infant nasal airway replicas was controlled using a lung simulator. Parameters for nasal cannula oxygen flow rate, nasal airway geometry, tidal volume, respiratory rate, inhalation/exhalation, or I:E ratio (ti/te), breath waveform, and cannula prong insertion position were varied to determine their effect on measured FiO2. In total, FiO2was measured for 384 different parameter combinations, with each combination repeated in triplicate. Analysis of variance (ANOVA) was used to assess the influence of parameters on measured FiO2. Results Measured FiO2was not appreciably affected by the breath waveform shape, the replica geometry, or the cannula position but was significantly influenced by the tidal volume, the inhalation time, and the nasal cannula flow rate. Conclusions The flow-weighted calculation overpredicted FiO2for measured values above 60%, but an empirical correction to the calculation provided good agreement with measured FiO2across the full range of experimental data.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".