Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will examine the current scenario of critical care medicine and describe trends for the future. RECENT FINDINGS: Critical care is facing increasing demands due to an aging population and the relative lack of intensivists. Quality and healthcare costs are becoming day-to-day issues. The future will see an increasing use of protocols, virtual consultations, and regionalized care for more complex and common diseases such as trauma and acute lung injury. Intensivists will be skeptical due to difficulties in demonstrating benefits of any new drug, ventilator, monitor, or laboratory test, when added to basic, life-saving treatments. We do not believe that a 'magic bullet' is soon to come, and emphasis will be placed on cost restraining. Computers will have an increasing presence in critical care, now eased by a user group that is increasingly adept at using them. However, ICUs will still rely on human resource, making the myth of a fully automated ICU bed unlikely. SUMMARY: The future of ICU will rely on management and teamwork. The costs of critical care will be restrained through the use of better management, guidelines, and skepticism regarding new technologies and drugs. Policy makers will help society build better strategies for critical care services.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".