Performance de glicosímetro utilizado no automonitoramento glicêmico de portadores de diabetes mellitus tipo 1
Bibliographic record
Abstract
This prospective study assessed the minimum volume of blood, the precision and the accuracy of capillary glycaemia obtained with the use of a digital glucometer. A total of 108 diabetic individuals were enrolled, teenagers and adults of both genders, from the Diabetes Clinic of the Royal Victoria Hospital, McGill University, Canada, in 6 months. Glycaemia monitoring was performed using an AccuCheck Compact (Roche) glucometer. For the volume, 6 samples of blood were tested, on three glucometers, using a crossover design (432 results). For accuracy, 100 samples of venous and arterial blood, measured with the glucometer and on a clinical laboratory were compared. For precision, 2 samples of venous blood and solution controls were repeatedly tested. Results demonstrated that a volume of 3.0 microL of capillary blood is sufficient for reproducible results. Measurements of venous and capillary glycaemia did not differ statistically when obtained with the glucometer or from a clinical laboratory (p > 0.05). Comparison of capillary glycaemia measured with the glucometer with venous and capillary glycaemia obtained from the laboratory resulted in a correlation coefficient of 0.9819 and 0.9842, respectively. These observations confirm the accuracy and precision of the tested glucometer. The establishment of a minimum digital punction of 3 microL may have positive impact upon the compliance to auto-monitoring routines.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".