Evaluation of three automatic oxygen therapy control algorithms on ventilated low birth weight neonates
Bibliographic record
Abstract
Neonates with under developed lungs often require oxygen therapy. During the course of oxygen therapy, elevated levels of blood oxygenation, hyperoxemia, must be avoided or the risk of chronic lung disease or retinal damage is increased. Low levels of blood oxygen, hypoxemia, may lead to permanent brain tissue damage and, in some cases, mortality. A closed loop controller that automatically administers oxygen therapy using 3 algorithms - state machine, adaptive model, and proportional integral derivative (PID) - is applied to 7 ventilated low birth weight neonates and compared to manual oxygen therapy. All 3 automatic control algorithms demonstrated their ability to improve manual oxygen therapy by increasing periods of normoxemia and reducing the need for manual FiO(2) adjustments. Of the three control algorithms, the adaptive model showed the best performance with 0.25 manual adjustments per hour and 73% time spent within target +/- 3% SpO(2).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".