Accuracy of Using a Point-of-Care Glucometer for Cerebrospinal Fluid Glucose Screening in Resource-Limited Countries
Bibliographic record
Abstract
OBJECTIVES: To explore the accuracy of using a point-of-care (POC) glucometer for cerebrospinal fluid (CSF) glucose screening. METHODS: A cross-sectional study was conducted. A glucose analysis of CSF samples collected from infants <90 days with suspected meningitis was paired between tests by using a POC glucometer (POC-CSF glucose) and a laboratory glucose analysis (laboratory-CSF glucose). Accuracy and limits of agreement were compared, as well as the glucometer performance to detect a laboratory-CSF glucose level <45 and 60 mg/dL. RESULTS: Seventy-three CSF samples were analyzed. Subjects' mean gestational age was 32.2 (SD 4.0) weeks, the mean weight was 1947.7 (SD 814.5) g, and the median age was 8 (interquartile range: 2 to 19.5) days. POC-CSF glucose levels ranged from 26 to 126 mg/dL. The mean (±1.96 SD) difference between POC-CSF and laboratory-CSF glucose levels was -1.6 (interquartile range: -12.6 to 9.4) mg/dL. A POC-CSF glucose level <45 mg/dL has a sensitivity and negative predictive value (NPV) to detect a laboratory-CSF glucose level <45 mg/dL of 82% and 94%, respectively. For a laboratory-CSF glucose level <60 mg/dL, a POC glucose level <60 mg/dL provides a sensitivity and NPV of 96% and 90%, respectively, whereas sensitivity and NPV reach 100% at a POC glucose level <70 mg/dL. CONCLUSIONS: A POC glucometer for CSF glucose can detect a potential abnormal glucose level with an appropriate cutoff level. This may facilitate rapid decisions for empirical antibiotics in suspected meningitis, pending laboratory results in limited-resource settings, but requires robust validation in future studies before implementation.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".