Human or monitor feedback to improve mask ventilation during simulated neonatal cardiopulmonary resuscitation
Bibliographic record
Abstract
OBJECTIVE: To investigate if external chest compressions (ECC) increase mask leak, and if human or technical feedback improves mask ventilation during simulated neonatal cardiopulmonary resuscitation (CPR). STUDY DESIGN: In this observational study, 32 participants delivered positive pressure ventilation (PPV) to a modified, leak-free manikin via facemask. Mask leak, tidal volume (VT), positive end expiratory pressure (PEEP) and respiratory rate (RR) were measured with a respiratory function monitor (RFM). Participants had to perform four studies. In the first study, participants performed PPV alone as baseline. Thereafter, three studies were performed in random order. In the PPV+ECC+manometer group, participants had to observe the manometer while the RFM was covered; in the PPV+ECC+RFM group, the RFM was used while the manometer was covered; and in the PPV+ECC+verbal feedback group, the RFM and manometer were covered while a team leader viewed the RFM and provided verbal feedback to the participants. RESULTS: Median (IQR) mask leak of all studies was 15% (5-47%). Comparing the studies, PPV+ECC+RFM and PPV+ECC+verbal feedback had significantly less mask leak than PPV+ECC+manometer. Mean (SD) VT of all studies was 9.5±3.5 mL. Comparing all studies, PPV+ECC+RFM had a significantly higher VT than PPV and PPV+ECC+manometer. As well, PPV+ECC+verbal feedback had a significantly higher VT than PPV. PEEP and RR were within our target, mean (SD) PEEP was 6±2 cmH2O and RR was 36±13/min. CONCLUSIONS: During simulated neonatal CPR, ECCs did not influence mask leak, and a RFM and verbal feedback were helpful methods to reduce mask leak and increase VT significantly.
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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 | 0.014 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".