Bibliographic record
Abstract
OBJECTIVE: To describe blood conservation strategies for critically ill patients. DATA SOURCES: By using a predefined strategy, we searched the electronic databases of Medline, EMBASE, CINAHL, the Cochrane database of systematic reviews, Cochrane central register of controlled trials, ACP Journal Club, Database of abstracts of reviews and effects, and HealthSTAR for descriptions and evaluations of strategies of blood conservation among critically ill patients. DATA SUMMARY: A number of blood conservation strategies have been used to prevent or treat anemia among critically ill patients. These include restrictive diagnostic phlebotomy using small-volume or pediatric phlebotomy tubes, point-of-care and inline bedside microanalysis, minimization of diagnostic sample waste, minimization of routine multiple daily phlebotomies, red blood cell salvage and antifibrinolytic agents for bleeding patients, consideration of removal of central venous and arterial catheters when no longer required for physiologic monitoring, threshold-based transfusion policy, and healthcare professional education. CONCLUSIONS: There are many strategies of blood conservation for critically ill patients. The effects of these strategies on phlebotomy volumes, hemoglobin and hematocrit levels, transfusion requirements, clinical outcomes, as well as intensive care unit and laboratory resources and costs should be further evaluated.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.005 | 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 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".