Pediatric Acute Lung Injury and Sepsis Investigators (PALISI): Evolution of an Investigator-Initiated Research Network
Bibliographic record
Abstract
The Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network originated over 20 years ago to foster research to optimize the care of critically ill infants and children. Over this period, PALISI has seen two major evolutions: formalization of our network infrastructure and a broadening of our clinical research focus. First, the network is unique in that its activities and meetings are funded by subscriptions from members who now comprise a multidisciplinary group of investigators from over 90 PICUs all over the United States (US) and Canada, with collaborations across the globe. In 2020, the network converted into a standalone, nonprofit organizational structure (501c3), making the PALISI Network formally independent of academic and clinical institutions or professional societies. Such an approach allows us to invest in infrastructure and future initiatives with broader opportunities for fund raising. Second, our research investigations have expanded beyond the original focus on sepsis and acute lung injury, to incorporate the whole field of pediatric critical care, for example, efficient liberation from mechanical ventilator support, prudent use of blood products, improved safety of intubation practices, optimal sedation practices and glucose control, and pandemic research on influenza and COVID-19. Our network approach in each field follows, where necessary, the full spectrum of clinical and translational research, including: immunobiology studies for understanding basic pathologic mechanisms; surveys to explore contemporary clinical practice; consensus conferences to establish agreement about literature evidence; observational prevalence and incidence studies to measure scale of a clinical issue or question; case control studies as preliminary best evidence for design of definitive prospective studies; and, randomized controlled trials for informing clinical care. As a research network, PALISI and its related subgroups have published over 350 peer-reviewed publications from 2002 through September 2022.
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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 | 0.197 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".