229-OR: A Calibration Protocol for Continuous Glucose Monitor Accuracy in the ICU
Bibliographic record
Abstract
Guidelines now recommend inpatient continuous glucose monitor (CGM) use, assuming that it is sufficiently accurate due to its apparent safety. The FDA issued a non-objection letter for inpatient CGM use, but it is not yet officially approved for hospitals, including in the ICU. To further study CGM in the ICU, we tested the accuracy of the Dexcom G6 (G6) in 32 adults in a medical-surgical ICU in Vancouver, BC using a calibration protocol in 1035 matched CGM and arterial point-of-care (POC) pairs. Our first uncalibrated study had a mean absolute relative differential (MARD) of 13.24%, which barely meets the critical care expert recommendation of MARD <14%. This study aims to show a higher accuracy can be achieved with a simple calibration protocol on Day 1, then none thereafter. The MARD for calibrated patients was 9.27%, significantly lower than 13.19% for uncalibrated patients (p<0.001). Calibration also had excellent safety with 100% of values within Clarke Error Grid Zones A and B compared to 99.07% without calibration. Our protocol achieved the lowest MARD and safest Clarke Error Grid profile of any ICU study comparing CGM to standard of care arterial POC and well exceeds the critical care expert recommendations. Our large sample of heterogenous critically ill patients also reached comparable accuracy to the MARD of 9% for G6 in outpatients. We believe our calibration protocol will allow CGM to be used with sufficient accuracy in the ICU. Disclosure S.Bann: None. J.C.Hercus: None. P.Atkins: None. A.Alkhairy: None. D.M.Thompson: None.
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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 | 0.036 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".