Methodology of the Second Pediatric Acute Lung Injury Consensus Conference
Bibliographic record
Abstract
OBJECTIVES: This article describes the methodology used for The Second Pediatric Acute Lung Injury Consensus Conference (PALICC-2). The PALLIC-2 sought to develop evidence-based clinical recommendations and when evidence was lacking, expert-based consensus statements and research priorities for the diagnosis and management of pediatric acute respiratory distress syndrome (PARDS). DATA SOURCES: Electronic searches were conducted using PubMed, Embase, and Cochrane Library (CENTRAL) databases from 2012 to March 2022. STUDY SELECTION: Content was divided into 11 sections related to PARDS, with abstract and full text screening followed by data extraction for studies which met inclusion with no exclusion criteria. DATA EXTRACTION: We used a standardized data extraction form to construct evidence tables, grade the evidence, and formulate recommendations or statements using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. DATA SYNTHESIS: This consensus conference was comprised of a multidisciplinary group of international experts in pediatric critical care, pulmonology, respiratory care, and implementation science which followed standards set by the Institute of Medicine, using the GRADE system and Research And Development/University of California, Los Angeles appropriateness method, modeled after PALICC 2015. The panel of 52 content and four methodology experts had several web-based meetings over the course of 2 years. We conducted seven systematic reviews and four scoping reviews to cover the 11 topic areas. Dissemination was via primary publication listing all statements and separate supplemental publications for each subtopic that include supporting arguments for each recommendation and statement. CONCLUSIONS: A consensus conference of experts from around the world developed recommendations and consensus statements for the definition and management of PARDS and identified evidence gaps which need further research.
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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 | 0.212 | 0.339 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".