Confirmation of successful supraglottic airway device placement in neonates using a respiratory function monitor
Bibliographic record
Abstract
BACKGROUND: This study investigated the use of a respiratory function monitor (RFM) to guide the placement of a supraglottic airway device (SAD) in neonates during intensive care interventions. We hypothesized that using a RFM would decrease the number of attempts needed for a successful placement. METHODS: This single-center pilot study was carried out at a tertiary NICU at the Medical University of Vienna. Patients were ventilated using a SAD during neurosurgical or endoscopic interventions. A RFM was either hidden (but recording) or visible to providers during SAD placement. Feedback from the RFM was used to assess correct/incorrect placement and optimize ventilation quality. The parameter leakage was used for assessment: if leak was <30%, correct placement was assumed. The primary outcome was the number of attempts until correct placement. Secondary outcomes included ventilation parameters recorded by the RFM and the duration of SAD placement. RESULTS: Six patients were included in this pilot trial. Using a RFM to guide SAD placement led to fewer attempts (median attempts: 3 [hidden] vs. 1 [visible]). Furthermore, using the RFM, necessary adaptations were made to the SAD position to decrease leakage (mean leakage: 74.8% [hidden] vs. 17.8% [visible]), subsequently endoscopy after insertion of SAD using the RFM then confirmed anatomically correct position. CONCLUSION: This pilot study indicated that a RFM might be useful to provide guidance during SAD placement. IMPACT STATEMENT: Feedback from a RFM reliably indicated correct anatomical placement of a SAD by correlating low leakage values with proper SAD positioning. RFM guidance could improve neonatal airway management, reducing procedural time and number of attempts. We present promising preliminary results. Further research is needed to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".