A Simulation Study to Assess the Effect of Analytic Error on Neonatal Glucose Measurements Using the Canadian Pediatric Society Position Statement Action Thresholds
Bibliographic record
Abstract
BACKGROUND: The Canadian Pediatric Society (CPS) has endorsed an algorithm for the screening and immediate management of babies at risk of neonatal hypoglycemia that provides time-dependent glucose concentration action thresholds. The objective of this study was to evaluate the impact of glucose analytic error (bias and imprecision) on the misclassification of glucose meter results from a neonatal intensive care unit (NICU) using the CPS guidelines. METHODS: = 23 749). Bias and imprecision were added to create measured glucose values. The percentages of measured glucose values that were misclassified at CPS action thresholds were determined by Monte Carlo simulation. RESULTS: Measurement biases ranging from -20 to +20 mg/dL combined with coefficients of variation 0% to 20% were evaluated to predict misclassification rates at 32, 36, and 47 mg/dL. The models demonstrated low risk of false normoglycemia-at 5% CV and +10 mg/dL bias: 0.8% to 5% misclassification at the 32 and 47 mg/dL thresholds due to bias. The models demonstrated risk of false hypoglycemia-at 5% CV and -10 mg/dL bias: 3% to 12.5% misclassification at 32 and 47 mg/dL thresholds due to both bias and imprecision. CONCLUSION: Using CPS action thresholds, the simulation model predicted the proportion of neonates at risk of inappropriate clinical action-both of omission or "failure to treat" and commission or "overtreatment" in response to NICU glucose meter results at specific bias and imprecision values.
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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 | 0.023 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".