Devices to Reduce the Volume of Blood Taken for Laboratory Testing in ICU Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Intensive care unit (ICU) patients are at high risk of anemia, which is associated with adverse clinical outcomes and death. Blood sampling for diagnostic testing is a potentially modifiable contributor to anemia. METHODS: We conducted a systematic review by searching MEDLINE and EMBASE from inception to October 5, 2017, for studies reporting the volume of blood taken for laboratory testing using blood sampling conservation devices compared to standard care or another intervention in adult ICU patients. RESULTS: We identified 8 eligible studies (n = 1204 patients) that used 2 types of devices: arterial access devices (n = 5) and reduced-volume blood collection tubes (n = 3). All studies reported a reduction in the volume of blood taken for laboratory testing with devices compared to standard practice (range 19%-80%). The studies were judged to have serious risk of bias, and due to heterogeneity, pooling for meta-analysis was not considered appropriate. CONCLUSIONS: Devices used to reduce the volume of blood taken for laboratory testing in ICU patients appear to be effective, although study heterogeneity limited our ability to calculate pooled estimates of efficacy for each device. Further assessment of clinical outcomes may establish clinical benefit with minimal negative consequences for hospitals and laboratories to facilitate the use of small-volume tubes.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".