Evaluation of a practice guideline for the management of respiratory distress syndrome in preterm infants: A quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: The use of mechanical ventilation to treat respiratory distress syndrome in preterm infants has been associated with the development of bronchopulmonary dysplasia. As part of a quality improvement initiative to reduce the incidence of bronchopulmonary dysplasia in preterm infants, a new practice guideline for the management of respiratory distress syndrome was developed and adopted into practice in a neonatal intensive care unit in February 2012. OBJECTIVE: To evaluate the effects of implementing the new guideline in regard to the use of mechanical ventilation and surfactant, and the incidence of bronchopulmonary dypslasia. METHODS: An historical cohort of very preterm infants (gestational age 26(0) to 32(6) weeks) born one year before guideline implementation was compared with a similar cohort of infants born one year following guideline implementation. Data were collected retrospectively from the local neonatal intensive care unit database. RESULTS: A total of 272 preterm infants were included in the study: 129 in the preguideline cohort and 143 in the postguideline cohort. Following the implementation of the guideline, the proportion of infants treated with ongoing mechanical ventilation was reduced from 49% to 26% (P<0.001) and there was a trend toward a reduction in bronchopulmonary dysplasia (27% versus 18%; P=0.07). There was no difference in the proportion of infants treated with surfactant (54% versus 50%). CONCLUSION: The implementation of the practice guideline helped to minimize the use of ongoing mechanical ventilation in preterm infants.
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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 | 0.089 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".