Ethical issues in pediatric emergency mass critical care
Bibliographic record
Abstract
INTRODUCTION: As a result of recent events, including natural disasters and pandemics, mass critical care planning has become a priority. In general, planning involves limiting the scope of disasters, increasing the supply of medical resources, and allocating scarce resources. Entities at varying levels have articulated ethical frameworks to inform policy development. In spite of this increased focus, children have received limited attention. Children require special attention because of their unique vulnerabilities and needs. METHODS: In May 2008, the Task Force for Mass Critical Care published guidance on provision of mass critical care to adults. Acknowledging that the critical care needs of children during disasters were unaddressed by this effort, a 17-member Steering Committee, assembled by the Oak Ridge Institute for Science and Education with guidance from members of the American Academy of Pediatrics, convened in April 2009 to determine priority topic areas for pediatric emergency mass critical care recommendations.Steering Committee members established subgroups by topic area and performed literature reviews of MEDLINE and Ovid databases. Draft documents were subsequently developed and revised based on the feedback from the Task Force. The Pediatric Emergency Mass Critical Care Task Force, composed of 36 experts from diverse public health, medical, and disaster response fields, convened in Atlanta, GA, on March 29-30, 2010. This document reflects expert input from the Task Force in addition to the most current medical literature. TASK FORCE RECOMMENDATIONS: The Ethics Subcommittee recommends that surge planning seek to provide resources for children in proportion to their percentage of the population or preferably, if data are available, the percentage of those affected by the disaster. Generally, scarce resources should be allocated on the basis of need, benefit, and the conservation of resources. Estimates of need, benefit, and resource utilization may be more subjective or objective. While the Subcommittee favors more objective methods, pediatrics lacks a simple, validated scoring system to predict benefit or resource utilization. The Subcommittee hesitantly recommends relying on expert opinion while pediatric triage tools are developed. If resources remain inadequate, they should then be allocated based on queuing or lottery. Choosing between these methods is based on ethical, psychological, and practical considerations upon which the Subcommittee could not reach consensus. The Subcommittee unanimously believes the proposal to favor individuals between 15 and 40 yrs of age is inappropriate. Other age-based criteria and criteria based on social role remain controversial. The Subcommittee recommends continued work to engage all stakeholders, especially the public, in deliberation about these issues.
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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 | 0.086 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".