Does Elimination of a Laboratory Sample Clotting Stage Requirement Reduce Overall Turnaround Times for Emergency Department Stat Biochemical Testing?
Bibliographic record
Abstract
Introduction: Laboratory turnaround times (TAT) influence length of stay for emergency department (ED) patients. We studied biochemistry TATs around the implementation of a plasma separating tube (PST) that omitted a 20-minute clotting step in processing when compared to the standard serum separating tubes (SST). Methods: We compared laboratory TATs using PST vs SST in a prospective before-and-after study with a washout period. TATs for creatinine, urea, electrolytes, troponin, and N-terminal pro b-type natriuretic peptide (NT-proBNP), as well as hemolysis rates, were collected for all ED patients. Results were excluded if the TAT was four minutes or less (data entry error). We recorded the 90th percentile response times (TAT90; the time for 90% of the tests to be completed). Statistical analysis used survival analyses, Mann-Whitney U tests, and Chi-square tests of independence. Results: SST and PST groups were matched for days of the week, critical values, or hemolysis. There was a statistically significant reduction in median TAT and proportion completed by 60 minutes. However, the effect size was only two to four minutes in the In-Lab-TAT90 with the PST tubes for all tests, except B-type natriuretic peptide (BNP). Conclusions: Reducing the machine processing time for stat blood work with PST tubes did not produce a clinically meaningful reduction of TAT. Clinically important improvement for Lab TAT requires process analysis and intervention that is inclusive of the entire system. Fractile response times at a 90th percentile for TAT within 60 minutes may be an accurate benchmark for analysis.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".