Clinical review: Critical care in the global context – disparities in burden of illness, access, and economics
Bibliographic record
Abstract
World health care expenditures exceed US $4 trillion. However, there is marked variation in global health care spending, from upwards of US $7,000 per capita in the US to under US $25 per capita in most of sub-Saharan Africa. In developed countries, care of the critically ill comprises a large proportion of health care spending; however, in developing countries, with a greater burden of both illness and critical illness, there is little infrastructure to provide care for these patients. There is sparse research to inform the needs of critically ill patients, but often basic requirements such as trained personnel, medications, oxygen, diagnostic and therapeutic equipment, reliable power supply, and safe transportation are unavailable. Why should this be a focus of intensivists of the developed world? Nearly all of those dying in developing countries would be our patients without the accident of latitude. Tailored to the needs of the region, the provision of critical care has a role, even in the context of limited preventive and primary care. Internationally and locally driven solutions are needed. We can help by recognizing the '10/90 gap' that is pervasive within global health care and our profession by educating ourselves of needs, contacting and collaborating with colleagues in the developing world, and advocating that our professional societies and funding agencies consider an increasingly global perspective in education and 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".