Bibliographic record
Abstract
Klonoff et al recently described a new glucose meter error grid.1 This error grid updates the limits for the various zones that contain clinically significant glucose meter errors. To determine these limits, clinicians were provided scenarios and were asked to describe glucose error levels that would prompt them to treat patients in several ways ranging from no treatment to emergency treatment. One could view this exercise as clinicians responding to a patient’s symptoms, or the threat of acute injury. Yet diabetes is a disease that includes the possibilities of acute injury and chronic injury due to persistent increases in glucose. Diabetic retinopathy is an example of serious chronic injury to patients with diabetes. Hemoglobin A1c levels that exceed 5.5% are associated with diabetic retinopathy.2 Yet an A1c level of 5.5% is equivalent to a mean glucose of 111 mg/dL.3 In error grid terms, a meter measuring glucose with a true level of 111 mg/dL, but reading 100 mg/dL, demonstrates an error in the A zone, which is the no-treatment-needed zone (this is the case for all of the popular glucose error grids including Clarke,4 Parkes,5 as well as the new surveillance error grid1). To be fair, the 5.5% level of A1c is the starting point for diabetic retinopathy but for an A1c level of 6.5% (equivalent to a mean glucose of 140 mg/dL), the prevalence of diabetic retinopathy doubles to 20%, yet this level of glucose bias is still in the A zone. One might argue that the possibility of a 10% to 40% consistent bias in modern-day glucose meters is unlikely. Yet, meters can exhibit biases due to interferences. Moreover, a large source of error for any assay, including glucose meters, is lot-to-lot reagent variability. Both of these errors can contribute to produce a consistent or fixed bias. Clearly one needs to inform about allowable glucose meter deviations associated with the treatment of acute injury and the proposed error grid does that superbly. We propose that another error grid is needed to inform about diabetes complications by providing allowable error limits for long-term bias. This situation has analogies in other areas such as preventive cardiology. An assay such as low density lipoprotein (LDL) cholesterol, which is not used to diagnose acute injury, might have limits set for allowable long-term bias to inform about the risk of coronary events associated with an incorrect LDL measurement.
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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 | 0.014 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".