Essential Emergency and Critical Care – a consensus among global clinical experts
Bibliographic record
Abstract
Abstract Background Globally, critical illness results in millions of deaths every year. Although many of these deaths are potentially preventable, the basic, life-saving care of critically ill patients are often overlooked in health systems. Essential Emergency and Critical Care (EECC) has been devised as the care that should be provided to all critically ill patients in all hospitals in the world. EECC includes the effective care of low cost and low complexity for the identification and timely treatment of critically ill patients across all medical specialities. This study aimed to specify the content of EECC and additionally, given the surge of critical illness in the ongoing pandemic, the essential diagnosis-specific care for critically ill patients with COVID-19. Methods A Delphi process was conducted to seek consensus (>90% agreement) in a diverse panel of global clinical experts. The panel was asked to iteratively rate proposed treatments and actions based on previous guidelines and the WHO/ICRC’s Basic Emergency Care. The output from the Delphi was adapted iteratively with specialist reviewers into a coherent and feasible EECC package of clinical processes plus a list of hospital resource requirements. Results The 269 experts in the Delphi panel had clinical experience in different acute medical specialties from 59 countries and from all resource settings. The agreed EECC package contains 40 clinical processes and 67 hospital readiness requirements. The essential diagnosis-specific care of critically ill COVID-19 patients has an additional 7 clinical processes and 9 hospital readiness requirements. Conclusion The study has specified the content of the essential emergency and critical care that should be provided to all critically ill patients. Implementation of EECC could be an effective strategy to reduce preventable deaths worldwide. As critically ill patients have high mortality rates, especially where trained staff or resources are limited, even small improvements would have a large impact on survival. EECC has a vital role in the effective scale-up of oxygen and other care for critically ill patients in the COVID-19 pandemic. Policy makers should prioritise EECC, increase its coverage in hospitals, and include EECC as a component of universal health coverage.
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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 | 0.202 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".