Filling in the GAPS: validation of anion gap (AGAP) measurement uncertainty estimates for use in clinical decision making
Bibliographic record
Abstract
OBJECTIVES: We compare measurement uncertainty (MU) calculations to real patient result variation observed by physicians using as our model anion gap (AGAP) sequentially measured on two different instrument types. An approach for discretely quantifying the pre-analytical contributions and validating AGAP MU estimates for interpretation of patient results is proposed. METHODS: AGAP was calculated from sodium, chloride, and bicarbonate reported from chemistry or blood gas analyzers which employ different methodologies and specimen types. AGAP MU was calculated using a top-down approach both assuming no correlation between measurands and alternatively, including consideration of measurand correlation. MU-derived reference change values (RCV) were calculated between chemistry and blood gas analyzers results. Observational paired AGAP data (n=39,626 subjects) was obtained from retrospectively analyzed specimens from five urban tertiary care hospitals in Calgary, Alberta, Canada. RESULTS: The MU derived AGAP RCV for paired specimen data by the two platforms was 5.2-6.1 mmol/L assuming no correlation and 2.6-3.1 mmol/L assuming correlation. From the paired chemistry and blood gas data, total observed variation on a reported AGAP has a 95% confidence interval of ±6.0 mmol/L. When the MU-derived RCV assuming correlation is directly compared against the observed distribution of patient results, we obtained a pre-analytical variation contribution of 2.9-3.5 mmol/L to the AGAP observed variation. In contrast, assuming no correlation leads to a negligible pre-analytical contribution (<1.0 mmol/L). CONCLUSIONS: MU estimates assuming no correlation are more representative of the total variation seen in real patient data. We present a pragmatic approach for validating an MU calculation to inform clinical decisions and determine the pre-analytical contribution to MU in this system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".