Caring for Critically Ill Patients in Humanitarian Settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Critical care medicine is far from the first medical field to come to mind when humanitarian action is mentioned, yet both critical care and humanitarian action share a fundamental purpose to save the lives and ease the suffering of people caught in acute crises. Critically ill children and adults will be present regardless of resource limitations and irrespective of geography, regional or cultural contexts, insecurity, or socioeconomic status, and they may be even more prevalent in a humanitarian crisis. Critical care is not limited to the walls of a hospital, and all hospitals will have critically ill patients regardless of designating a specific ward an ICU. Regular and consistent consideration of critical care principles in humanitarian settings provides crucial guidance to intensivists and nonintensivists alike. A multidisciplinary, systematic approach to patient care that encourages critical thinking, checklists that encourage communication among team members, and context-specific critical care rapid response teams are examples of critical care constructs that can provide high-quality critical care in all environments. Promoting critical care principles conveys the message that critical care is an integral part of health care and should be accessible to all, no matter the setting. These principles can be effectively adopted in humanitarian settings by normalizing them to everyday clinical practice. Equally, core humanitarian principles-dignity, accountability, impartiality, neutrality-can be applied to critical care. Applying principles of critical care in a context-specific manner and applying humanitarian principles to critical care can improve the quality of patient care and transcend barriers to resource limitations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it