76: Minimizing Blood Losses and Reducing the Need for Transfusions in Low Birth Weight Infants: A Scoping Review of the Literature
Bibliographic record
Abstract
Premature infants frequently develop anemia and often require transfusions. Packed red blood cells transfusions can be associated with complications, some potentially life-threatening. Best evidence of strategies to reduce blood loss and red blood cells transfusions has not previously been summarized in a single review and knowledge gaps in this area are unclear. To perform a scoping review of the evidence on blood saving practices and strategies to reduce red blood cells transfusions in premature neonates. Standard methodology for scoping reviews was followed. MEDLINE (1946 to April 15, 2013), Embase (1947 to April 15, 2013), and Cochrane Central Register of Controlled Trials (CENTRAL, Issue 1, March 2013) were searched using the Ovid interface. The target population included premature infants (<1500g or <37 weeks) during initial hospital admission. Interventions included recombinant human erythropoietin, the use of defined thresholds for transfusion, delayed cord clamping or cord milking, iron supplementation, techniques to obtain blood samples (capillary, venous or arterial), point of care testing and non-invasive monitoring techniques. Titles, abstract and full publications to include were independently reviewed by two of the authors. A total of 1353 records were identified; 138 abstracts were selected and 37 full publications were reviewed and included in this review (Table 1). High quality evidence such as systematic reviews and randomized controlled trials have been performed for some interventions (erythropoietin, transfusion thresholds, delayed cord clamping and iron supplementation). Early erythropoietin and delayed cord clamping have been shown to reduce the need for blood transfusions. For blood sampling techniques and non-invasive monitoring, evidence is limited to prospective cohort studies and case series. Non invasive monitoring has shown variable reliability when compared to standard methods and point of care testing can reduce blood volume losses and the need for transfusions. Sufficient high quality evidence exists demonstrating that erythropoietin, transfusion thresholds, the use of delayed cord clamping reduce the need for transfusions. A gap has been identified in good quality research for strategies such as methods of point of care testing and non-invasive monitoring.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".