Underfilled tubes revisited: What blood tests can be reported on short draws?
Bibliographic record
Abstract
OBJECTIVE: Underfilled blood tubes (short draws) are often collected from children or those with poor venous access. In a pilot study, we investigated which tests among a large acute care panel could be reported on short draws. METHODS: Blood was drawn in BD vacutainers (short draw: 1 mL [33%-56% fill volume] vs complete draw: 1.8-3 mL [100% fill volume]) from 12 volunteers for 3 coagulation tests, 36 chemistry tests, and the complete blood count (CBC) with differential. Tests that were strong candidates for reporting did not have statistically significant biases between short and complete draws, whereas potential candidates had statistically significant biases that were small (<25% of total allowable error and less than desirable bias from biological variation). Biases that increased or decreased across concentration ranges invalidated reporting candidacy. RESULTS: Two coagulation tests, 14 chemistry tests, and 15 CBC components were strong candidates for reporting. There were 9 chemistry tests and 2 CBC components that were potential candidates for reporting. CONCLUSIONS: Underfilled blood tubes, or short draws, may be valid collections for several coagulation, chemistry, and hematology tests-which may prevent additional unnecessary phlebotomy. Laboratories should perform their own studies to determine if short draws are acceptable for limited testing using their tube and instrument types.
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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 | 0.085 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".