Falsely elevated point-of-care lactate measurement after ingestion of ethylene glycol
Bibliographic record
Abstract
BACKGROUND: A patient presented with severe acidosis and a point-of-care lactate measurement of 42 mmol/L. Mesenteric ischemia was suspected, with a potential need for laparotomy; however, plasma lactate measurements were below 4 mmol/L. Ethylene glycol ingestion was subsequently diagnosed. We therefore wished to determine why discrepancies in lactate measurements occur and whether this "lactate gap" could be clinically useful. METHODS: We phlebotomized blood, added various concentrations of metabolites of ethylene glycol, and tested the resulting samples with the 5 most common lactate analyzers. RESULTS: With the Radiometer 700 point-of-care analyzer, glycolate addition resulted in an artifactual, massive lactate elevation, even at low glycolate concentrations. Another major ethylene glycol metabolite, glyoxylate (but not oxalate or formate), caused similar elevations. The i-STAT and Bayer point-of-care analyzers and the Beckman and Vitros laboratory analyzers reported minimal lactate elevations. Lactate gap was determined by comparing the Radiometer result with the corresponding result from any of the other analyzers. INTERPRETATION: We demonstrated how inappropriate laparotomy or delayed therapy might occur if clinicians are unaware of this phenomenon or have access to only a single analyzer. We also showed that lactate gap can be exploited to expedite treatment, diagnose late ethylene-glycol ingestion and terminate dialysis. By comparing lactate results from the iSTAT or Bayer devices with that from the Radiometer, ethylene-glycol ingestion can be diagnosed at the point of care. This can expedite diagnosis and treatment by hours, compared with waiting for laboratory results for plasma ethylene glycol.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".