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
PURPOSE OF REVIEW: Interest in the global burden of critical illness is growing, but comprehensive data to describe this burden and the resources available to provide care for critically ill patients are lacking. RECENT FINDINGS: Challenges to obtaining population-based global estimates of critical illness and resources to treat it include the syndrome-based definitions of critical illness, incorrect equating of 'critical illness' with 'admission to an intensive care unit', lack of reliable case ascertainment in administrative data, and short prodrome and high mortality of critical illness, limiting the number of prevalent cases. Modeling techniques will be required to estimate the burden of critical illness and disparities in access to critical care using existing data sources. Demand for critical care is likely to increase, related to urbanization, an aging demographic, and the ongoing wars, disasters, and pandemics, whereas economic crises will likely decrease the ability to pay for it. SUMMARY: Major unexplored research and public health questions remain unanswered regarding the worldwide burden of critical illness, variation in resources available for treatment, and strategies to prevent and treat critical illness that are broadly effective and feasible.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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