Outpatient Phlebotomy Success and Reasons for Specimen Rejection
Bibliographic record
Abstract
Abstract Objectives.—To determine the rate with which blood collection is successful on the initial phlebotomy encounter, the rate with which laboratory personnel judge specimens unsuitable for analysis, and the practice characteristics associated with fewer unsuccessful collections and fewer rejected specimens. Design.—Clinical laboratories participating in the College of American Pathologists Q-Probes laboratory improvement program prospectively characterized the outcome of outpatient phlebotomies for 3 months or until 20 unsuccessful phlebotomy encounters occurred. By questionnaire, participants provided information about test ordering, patient preparation, and specimen collection. Setting and Participants.—Institutions in the United States (n = 202), Canada (n = 4), Australia (n = 3), and South Korea (n = 1). Main Outcome Measures.—Percentage of successful encounters and percentage of unsuitable specimens. Results.—Of 833289 encounters, 829723 were successful. Phlebotomies were unsuccessful because patients were not fasting as directed (32.2%), phlebotomy orders were missing information (22.5%), patients specimens were difficult to draw (13.0%), patients left the collection area before specimens were collected (11.8%), patients were improperly prepared for reasons other than fasting (6.3%), patients presented at the wrong time (3.1%), or for other reasons (11.8%). Only 2153 specimens (0.3%) were unsuitable; these samples were hemolyzed (18.1%), of insufficient quantity (16.0%), clotted (13.4%), lost or not received in the laboratory (11.5%), inadequately labeled (5.8%), at variance with previous or expected results (4.8%), or unacceptable for other reasons (31.1%). Facilities staffed by laboratory-administered phlebotomists reported higher success rates than facilities staffed by nonlaboratory-administered phlebotomists (P = .002). Conclusions.—Most outpatient phlebotomy encounters are successful and result in specimens suitable for laboratory 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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".