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Blood conservation in the intensive care unit

2003· review· en· W2024171065 on OpenAlexaff
Robert Fowler, Matthew Berenson

Bibliographic record

VenueCritical Care Medicine · 2003
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePhlebotomyIntensive care medicineIntensive care unitCochrane LibraryCINAHLMEDLINEHematocritRandomized controlled trialSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.886
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.110
GPT teacher head0.407
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations65
Published2003
Admission routes1
Has abstractyes

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