Electrochemical Biosensor for Glycated Hemoglobin (HbA1c)
Bibliographic record
Abstract
Diabetes is recognized as a group of heterogeneous disorders with the common elements of hyperglycaemia and glucose intolerance due to insulin deficiency, impaired effectiveness of insulin action or both (Harris & Zimmet, 1997). If left untreated or improperly managed, diabetes can result in a variety of complications, including heart disease, kidney disease, eye disease, impotence and nerve damage. Diagnosis and management of the disease require a tight monitoring of blood glucose levels that serves a number of purposes: provides a quick measurement of blood glucose level at a given time. determines if a diabetic person has a high or low blood glucose level at a given time. demonstrates the link between lifestyle, medication and blood glucose levels. helps diabetics and diabetes health-care teams make changes to lifestyle and medication that will improve blood glucose levels. Electrochemical biosensors for glucose (glucose meters) play a leading role for this purpose. For the purpose of measuring daily glucose levels to control food intake and insulin usage, these glucose meters work although some difficulties exist. For example, blood glucose level measurements are recommended three to four times per day. Due to the large fluctuations in glucose levels that naturally occur over the course of a day, measurements on an empty stomach and within 2 h of eating are required for comparison purposes. These problems are more prominent for the diagnosis of diabetes and determining the link between lifestyle and medication once a patient has been diagnosed with this disease. Historically, measurement of glucose levels has been the method universally used to diagnose diabetes. Laboratory methods such as fasting plasma glucose (FPG) or 2-h plasma glucose (2HPG) level have been used for this purpose. However, this approach still suffers from the same problems and difficulties associated with glucose biosensors such as the need for fasting, biological variability and the effects of acute perturbations (e.g., stressor illnessrelated) on glucose levels. It has recently been concluded that the best marker for long term glycaemic control is whole blood glycated hemoglobin (i.e., hemoglobin A1c denoted as HbA1c) since its levels respond to the long-term progression of diabetes without the shortterm fluctuations characteristic of glucose (Berg & Sacks, 2008). Also, the use of this approach solves many of the problems associated with FPG or 2HPG methods based on glucose measurements such as no need for fasting, substantially less biological variability and relative insensitivity of HbA1c levels to acute perturbations. On the other hand with advances in instrumentation and standardization, the accuracy and precision of A1C assays
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".