Hematocrit correction does not improve glucose monitor accuracy in the assessment of neonatal hypoglycemia
Bibliographic record
Abstract
BACKGROUND: The lack of accuracy of point of care (POC) glucose monitors has limited their use in the diagnosis of neonatal hypoglycemia. Hematocrit plays an important role in explaining discordant results. The objective of this study was to to assess the effect of hematocrit on the diagnostic performance of Abbott Precision Xceed Pro (PXP) and Nova StatStrip (StatStrip) monitors in neonates. METHODS: All blood samples ordered for laboratory glucose measurement were analyzed using the PXP and StatStrip and compared with the laboratory analyzer (ABL 800 Blood Gas analyzer [ABL]). Acceptable error targets were ±15% for glucose monitoring and ±5% for diagnosis. RESULTS: A total of 307 samples from 176 neonates were analyzed. Overall, 90% of StatStrip and 75% of PXP values met the 15% error limit and 45% of StatStrip and 32% of PXP values met the 5% error limit. At glucose concentrations ≤4 mmol/L, 83% of StatStrip and 79% of PXP values met the 15% error limit, while 37% of StatStrip and 38% of PXP values met the 5% error limit. Hematocrit explained 7.4% of the difference between the PXP and ABL whereas it accounted for only 0.09% of the difference between the StatStrip and ABL. The ROC analysis showed the screening cut point with the best performance for identifying neonatal hypoglycemia was 3.2 mmol/L for StatStrip and 3.3 mmol/L for PXP. CONCLUSIONS: Despite a negligible hematocrit effect for the StatStrip, it did not achieve recommended error limits. The StatStrip and PXP glucose monitors remain suitable only for neonatal hypoglycemia screening with confirmation required from a laboratory analyzer.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".