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Record W2151039338 · doi:10.1177/1932296814543662

Acute Versus Chronic Injury in Error Grids

2014· letter· en· W2151039338 on OpenAlexaff
Jan S. Krouwer, George S. Cembrowski

Bibliographic record

VenueJournal of Diabetes Science and Technology · 2014
Typeletter
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsAlberta HealthAlberta Health Services
Fundersnot available
KeywordsMedicineDiabetes mellitusGlucose meterDiabetic retinopathyRetinopathyInternal medicineEmergency medicineAlgorithmEndocrinologyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.417
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.317
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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