Accuracy of point-of-care testing devices for haemoglobin in the operating room: meta-analysis
Bibliographic record
Abstract
BACKGROUND: Point-of-care tests (POCT) for haemoglobin are increasingly used to guide intraoperative transfusion. However, their accuracy compared to central laboratory tests is unknown. The objective was to perform a systematic review and meta-analysis of method comparison studies assessing the accuracy of POCT versus central laboratory haemoglobin tests in patients undergoing surgery. METHODS: Electronic databases were searched from inception to April 2020 (updated August 2023). Any methodological approach comparing haemoglobin measurements between POCT and central laboratory in patients undergoing surgery under anaesthesia in the operating room were included. Data abstraction was guided by PRISMA and risk of bias was assessed by QUADAS-2. Data were extracted independently and in duplicate by two reviewers. Outcomes included mean differences between POCT and central laboratory haemoglobin with associated standard deviations and 95% limits of agreement (LOA). RESULTS: Of 3057 citations, 34 studies were included (n = 2427, 6857 paired measurements). Several devices were compared (pulse co-oximetry, n = 25; HemoCue, n = 10; iSTAT, n = 6; blood gas analysers, n = 10; haematology analyser, n = 2). Median sample size was 41 patients, and 11 studies were funded by device manufacturers. Fifteen of 34 studies had low risk of bias. Pooled mean differences (95% LOA) were: pulse co-oximeters 2.3 g/l (-25.2-29.8), HemoCue -0.3 g/l (-11.1-10.5), iSTAT -0.3 g/l (-8.4-7.8) and blood gas analysers -2.6 g/l (-17.8-12.7). CONCLUSION: All POCT examining intraoperative haemoglobin measurement yielded pooled mean difference LOAs larger than the allowable limit difference of ±4 g/dl. Intraoperative haemoglobin measured by POCT should not be considered interchangeable with central laboratory values and caution is necessary when using these tests to guide intraoperative transfusion.
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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 | 0.042 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.059 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".