Critical care resource allocation: trying to PREEDICCT outcomes without a crystal ball
Bibliographic record
Abstract
Despite pandemic influenza's long reign atop the list of potential medical catastrophes, the first protocol designed to support critical care triage in a pandemic was published only in 2006. InFACT (the International Forum of Acute Care Trialists) was formed in 2009 and provided a platform for international critical care research collaboration during the 2009-2010 Influenza A(H1N1) pandemic. Over the past 2 years, a number of working groups have emerged from InFACT focused upon improving the investigation and care of patients with severe respiratory illness. Arising from these efforts, in June of 2012, an international group of clinicians convened the first meeting of the PREEDICCT (Providing Resources for Effective and Ethical Decisions In Critical Care Triage) study group. The group's aim is to develop decision support tools appropriate for use in triaging critically ill adult patients during epidemics, mass-casualty scenarios or other resource limited settings. This meeting identified a number of knowledge gaps and research priorities in this area, and suggested a revised framework for the requirements of an adequate triage decision support tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.020 | 0.038 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".