Bibliographic record
Abstract
PURPOSE OF REVIEW: Although critically ill patients are at high risk of venous thromboembolism and bleeding, and thromboprophlyaxis is of proven effectivity in other settings, there remain relatively few data to assist clinicians in providing evidence-based care for medical-surgical patients in the intensive care unit. RECENT FINDINGS: Deep vein thrombosis occurs in 5-10% of critically ill patients even if they receive unfractionated heparin for prophylaxis. Both heparin and low molecular weight heparin can be safely administered to the majority of critically ill patients and the low molecular weight heparin dalteparin does not appear to bioaccumulate even when administered to patients with severe renal dysfunction. Further research is currently underway to better define how these conditions can be optimally treated. SUMMARY: Despite the high morbidity and mortality because of critical illness, the risk of venous thromboembolism in these patients, and adverse outcomes due to venous thromboembolism, much more methodologically rigorous data are required in the form of large, well designed randomized trials before firm recommendations about prophylaxis can be provided to this highly vulnerable population.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".