Impaired clinical utility of sequential patient GEM blood gas measurements associated with calibration schedule
Bibliographic record
Abstract
Background Within- and/or between-instrument variation may falsely indicate patient trends or obscure real trends. We employ a methodology that transforms sequential intra-patient results into estimates of biologic and analytic variation. We previously derived realistic biologic variation (s b ) of blood gas (BG) and hematology analytes. We extend this methodology to derive the imprecision of two GEM 4000 BG analyzers. Methods A laboratory data repository provided arterial BG, electrolyte and metabolite results generated by two GEM 4000s on ICU patients in 2012–2013. We tabulated consecutive pairs of intra-patient results separated by increasing time interval between consecutive tests. The average between pair variations were regressed against time with the y-intercept representing the sum of the biologic variation and short term analytic variation: y o 2 = s b 2 + s a 2 . Using an equivalent equation for the Radiometer ABL, the imprecision of the two GEMs was calculated: s aGEM = (y oGEM 2 − y oABL 2 + s aABL 2 ) 1/2 . This analysis was performed for nearly all measurements, regardless of time as well for values obtained over two 12 h mutually exclusive periods, starting either at 2 am or 2 pm. Results Regression graphs were derived from 1800 patients' blood gas results with least 10,000 data pairs grouped into 2 h intervals. The calculated s aGEM exceed the directly measured s aABL with many GEM sigma ratios of biologic variation/analytic variation being close to unity. All of the afternoon s aGEM exceeded their morning counterparts with pH, pCO 2 , K and bicarbonate being statistically significant. Conclusion For many analytes, the average analytical variation of tandem GEMs approximates the biologic variation, indicating impaired clinical usefulness of tandem sequential measurements. A significant component of this variation is due to increased variation of the GEMs between 2 pm and 2 am.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".