Extremely low gestational age infants: Developing a multidisciplinary care bundle
Bibliographic record
Abstract
Abstract Background Clinical experience in managing extremely low gestational age infants, particularly those born <24 weeks’ gestation, is limited in Canada. Our goal was to develop a bedside care bundle for infants born <26 weeks’ gestation, with special considerations for infants of <24 weeks, to harmonize and improve quality of care. Methods We created a multidisciplinary working group with experience in caring for preterm infants, searched the literature from 2000 to 2019 to identify best practices for the care of extremely preterm infants and consulted colleagues across Canada and internationally. Iterative improvements were made following the Plan-Do-Study-Act methodology. Results A care bundle, created in October 2015, was divided into three time periods: initial resuscitation/stabilization, the first 72 hours and days 4 to 7, with each period subdivided in 8 to 12 care themes. Revisions and practice changes were implemented to improve skin integrity, admission temperature, timing of initiation of feeds, reliability of transcutaneous CO2 monitoring and ventilation. Of 127 infants <26 weeks admitted between implementation and end of 2019, 78 survived to discharge (61%). Conclusion It will be important to determine, with ongoing auditing and further evaluation, whether our care bundle led to improvements of short- and long-term outcomes in this population. Our experience may be useful to others caring for extremely low gestational age 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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".