Extracorporeal membrane oxygenation for respiratory failure in children: the years before and after the 2009 H1N1 pandemic
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether there was any impact on the number of pediatric extracorporeal membrane oxygenation runs and survival rates in the years subsequent to the 2009 pandemic. METHODS: We studied two different periods of extracorporeal membrane oxygenation support for respiratory failure in children by analyzing datasets from the Extracorporeal Life Support Organization. Autoregressive integrated moving average models were constructed to estimate the effect of the pandemic. The year 2009 was the year of intervention (the H1N1 epidemic) in an interrupted time series model. Data collected from 2001 - 2010 were considered preintervention, and data collected from 2010 - 2017 were considered postintervention. RESULTS: There was an increase in survival rates in the period 2010 - 2017 compared to 2001 - 2010 (p < 0.0001), with a significant improvement in survival when extracorporeal membrane oxygenation was performed for acute respiratory failure due to viral pneumonia. The autoregressive integrated moving average model shows an increase of 23 extracorporeal membrane oxygenation runs per year, prior to the point of the level effect (2009). In terms of survival, the preslope shows that there was no significant increase in survival rates before 2009 (p = 0.41), but the level effect was nearly significant after two years (p = 0.05), with a 6% increase in survival. In four years, there was an 8% (p = 0.03) increase in survival, and six years after 2009, there was up to a 10% (p = 0.026) increase in survival. CONCLUSION: In the years following 2009, there was a significant, global incremental increase in the extracorporeal membrane oxygenation survival rates for all runs, mainly due to improvements in the technology and treatment protocols for acute respiratory failure related to viral pneumonia and other respiratory conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".