Evaluation of i-STAT Creatinine Assay
Bibliographic record
Abstract
BACKGROUND: We evaluated a new, 2-min blood creatinine method using the hand-held i-STAT analyzer. Good results have already been reported using this analyzer for 10 methods including electrolytes, TCO2, pH, PCO2, bicarbonate, glucose, hemoglobin and urea for uremic blood, hemodialysate and peritoneal effluent. METHODS: Evaluation included study of imprecision and accuracy. RESULTS: Imprecision studies gave excellent results, including those for reproducibility of 6 solutions with a mean of 10 repeats and coefficients of variation (CVs) of 0.4-3.4%, and also the mean of the differences between 33 duplicate blood specimens which was 2.2% of the specimen mean. To assess accuracy, we compared results of 149 tests by i-STAT and Beckman Synchron CX7 methods. The difference between the two means was 2.6% and the mean of all differences was 10.9% with i-STAT results higher, especially when blood creatinine values were < 100 micromol/l (1.1 mg/dl) indicating the need for a slightly higher upper limit of the normal range. The correlation coefficient between the two methods was 0.99, the slope 1.0 and the intercept -5.0 micromol/l (-0.06 mg/dl). We assessed the recommended creatinine correction for variation in PCO2 above and below 40 mm Hg, but our results did not suggest the need for such a correction in our range of 27-64 mm Hg; omission would remove a major method disadvantage. Assays of hemodialysate and peritoneal effluent were also satisfactory. CONCLUSIONS: The i-STAT creatinine method is simple and rapid and our evaluation showed satisfactory accuracy and precision. However, results were on average slightly higher than for the Beckman Synchron CX7 method.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".