An electronic technetium-99m-diethylenetriaminepentaacetic acid glomerular filtration rate spreadsheet with novel embedded quality assurance features
Bibliographic record
Abstract
BACKGROUND: Critical clinical decisions are made on the basis of the glomerular filtration rate (GFR) measured using technetium-99m-diethylenetriaminepentaacetic acid (DTPA) administration, followed by multiple time-point plasma sampling. As GFR studies rely on few data points and produce a single result, they are prone to technical errors that may remain inconspicuous. OBJECTIVE: We describe a data analysis worksheet that provides real-time quality control (QC) indicators and evaluate our initial clinical experience. METHODS: Two hundred and forty-six consecutive GFR studies carried out at our clinics were included. Our protocol used plasma samples at 2, 3, and 4 h after injection of technetium-99m-DTPA. Duplicate plasma samples, background samples, and aliquots of an activity dilution standard were counted. Times were logged for injection and dilution standard preparation, blood sampling, and counting. Data were entered into a custom GFR analysis spreadsheet that flagged QC in real time at warning and error levels, including QC of the expected ratio between dilution standard counts-activity ratio (CARs) measurements, which was newly introduced to our clinic. The prevalence of QC events was analyzed in three phases: baseline, training, and evaluation (n=31, 69, and 146, respectively). RESULTS: From the baseline and training phases (n=100), CAR reference values were determined for each of two sites. In the absence of the CAR QC indicator, errors were present in 5/31 (16%) examinations, but with QC indication decreased to 7/146 (5%) (P<0.05), suggesting that the real-time QC information guided the technologists to ensure proper standard preparation and sample handling, as intended. Improvements in other QC measures were also noted, resulting in an overall error rate reduction from 23 to 8%. CONCLUSION: Real-time analysis of redundant information as a component of the GFR worksheet ensures quality results, but training of technologists and interpreting physicians is essential for optimal utilization of these QC indicators.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".