Bibliographic record
Abstract
BACKGROUND: Effective positive pressure ventilation (PPV) via facemask is dependent on adequate mask ventilation technique. Mask leak and airway obstruction can affect effective PPV. The Neonatal Resuscitation Program (NRP) emphasizes that during PPV corrective steps “MR. SOPA” should be performed to ensure effective mask PPV. However, skills taught during a NRP-course generally decline shortly afterwards and often do not result in proficiency during mask PPV. OBJECTIVES: We hypothesized that daily simulation drills to teach the MR. SOPA corrective steps will improve mask ventilation performance in preterm infants <33 weeks requiring PPV at birth. DESIGN/METHODS: After a 4-month observation period (baseline), we randomly assigned 58 healthcare members from the local neonatal resuscitation team to either daily MR. SOPA drills or self-directed revision of the 2015 NRP algorithm over a period of 3 months (intervention phase). This was followed by a 4-month post-observation phase (post-training phase). During baseline and post-training phase resuscitations of infants <33weeks gestation were recorded using our unique recording system (incl. respiratory function monitoring and video recordings). Mask ventilation performance and use of MR. SOPA were compared between pre and post intervention phase. RESULTS: Overall, there was a significant increase in efficiently and correctly applying MR. SOPA corrective steps during PPV in the MR. SOPA drill group. There was no difference in the rate of delivery room intubation post intervention. There was no difference on days of respiratory support following admission to NICU or neonatal death at discharge. CONCLUSION: Healthcare provider receiving daily MR. SOPA drills had improved mask ventilation technique and performance compared to self-directed NRP learning.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".