Hematocrit Interference of Blood Glucose Meters for Patient Self-Measurement
Bibliographic record
Abstract
BACKGROUND: Abnormal hematocrit levels may interfere with glucose readings of patient self-assessment blood glucose (BG) meters. The aim of this laboratory investigation was to assess the potential influence of hematocrit variations on a variety of BG meters applying different measurement technologies. METHODS: Venous heparinized blood was manipulated to contain three different BG concentrations (50-90, 120-180, and 280-350 mg/dl) and five different hematocrit levels (25%, 35%, 45%, 55%, and 65%). After careful oxygenation to normal blood oxygen pressure (65-100 mmHg), each sample was measured (eight times) with the following devices: Accu-Chek® Aviva Nano and Active, Breeze®2 and Contour®, FreeStyle Freedom Lite®, GlucoDr. auto™, Glucofix® mio Plus, GlucoLab™, GlucoMen® LX Plus, Nova Max® Link, Nova Max® Plus, OneTouch® Ultra®2 and Verio®, On Call® Plus and Platinum, Optium Xceed®, Precision Xceed®, and TaiDoc Fora TD-4227. A YSI 2300 STAT Plus™ glucose analyzer served as reference method. Stability to hematocrit influence was assumed, with <10% mean glucose result bias between the highest and lowest hematocrit levels. RESULTS: Six of the investigated meters showed a stable performance in this investigation: Accu-Chek Active (7%), Glucofix mio Plus (5%), GlucoMen LX Plus (4%), Nova Max Plus (4%), Nova Max Link (7%), and OneTouch Verio (3%). All other meters failed this hematocrit interference test, with FreeStyle Freedom Lite (11%), and On Call Platinum (12%) being the better devices and On Call Plus (68%), GlucoLab (51%), TaiDoc Fora TD-4227 (39%), and Breeze 2 (38%) showing the worst performance. CONCLUSIONS: Hematocrit may affect BG meter performance in daily routine. In case of interference, low hematocrit values (<35%) result in too high readings. Our results encourage use of meters that are not affected by hematocrit interference.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".