Strategies to Reduce Diagnostic Blood Loss and Anemia in Hospitalized Patients: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Blood sampling is a recognized contributor to hospital-acquired anemia. We aimed to bundle all published neonatal, pediatric, and adult data regarding clinical interventions to reduce diagnostic blood loss. DATA SOURCES: Four electronic databases were searched for eligible studies from inception until May 2021. STUDY SELECTION: Two reviewers independently selected studies, using predefined criteria. DATA EXTRACTION: One author extracted data, including study design, population, period, intervention type and comparator, and outcome variables (diagnostic blood volume and frequency, anemia, and transfusion). DATA SYNTHESIS: Of 16,132 articles identified, we included 39 trials; 12 (31%) were randomized controlled trials. Among six types of interventions, 27 (69%) studies were conducted in adult patients, six (15%) in children, and six (15%) in neonates. Overall results were heterogeneous. Most studies targeted a transfusion reduction ( n = 28; 72%), followed by reduced blood loss ( n = 24; 62%) and test frequency ( n = 15; 38%). Small volume blood tubes ( n = 7) and blood conservation devices ( n = 9) lead to a significant reduction of blood loss in adults (8/9) and less transfusion of adults (5/8) and neonates (1/1). Point-of-care testing ( n = 6) effectively reduced blood loss (4/4) and transfusion (4/6) in neonates and adults. Bundles including staff education and protocols reduced blood test frequency and volume in adults (7/7) and children (5/5). CONCLUSIONS: Evidence on interventions to reduce diagnostic blood loss and associated complications is highly heterogeneous. Blood conservation devices and smaller tubes appear effective in adults, whereas point-of-care testing and bundled interventions including protocols and teaching seem promising in adults and children.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".