Human-Derived D-Dimer for External Quality Assessment
Bibliographic record
Abstract
External quality assessment (EQA) of D-dimer assays has been limited by a lack of standardized human-derived testing material. In 2006 and 2007, the Quality Management Program--Laboratory Services, Toronto, Canada, investigated the use of commercially prepared lyophilized human plasma spiked with human-derived D-dimer components manufactured by Affinity Biologicals, Hamilton, Canada. Four surveys were performed. Participants reported the level or presence of D-dimer using quantitative or qualitative methods. Participants performing quantitative testing provided their unit of measure and reference interval. Results were considered correct if they fell within the range appropriate for each sample (normal/negative or abnormal/positive). Overall, survey results were excellent, with 4.0% (95% confidence interval [CI], 1.3%-9.1%), 0.8% (CI, 0.0%-1.5%), 2.3% (CI, 0.5%-6.6%), and 2.3% (CI, 0.4%-6.6%) of participants reporting an incorrect result in the first, second, third, and fourth surveys, respectively. A commercially prepared D-dimer is a suitable material for EQA testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".