Report of an International Survey of Molecular Genetic Testing Laboratories
Bibliographic record
Abstract
OBJECTIVE: To collect data on the practices of molecular genetic testing (MGT) laboratories for the development of national and international policies for quality assurance (QA). METHODS: A web-based survey of MGT laboratory directors (n = 827; response rate 63%) in 18 countries on 3 continents. QA and reporting indices were developed and calculated for each responding laboratory. RESULTS: Laboratory setting varied among and within countries, as did qualifications of the directors. Respondents in every country indicated that their laboratory receives specimens from outside their national borders (64%, n = 529). Pair-wise comparisons of the QA index revealed a significant association with the director having formal training in molecular genetics (p < 0.005), affiliation with a genetics unit (p = 0.003), accreditation of the laboratory (p < 0.005) and participation in proficiency testing (p < 0.005). Research labs had a lower mean report score compared to all other settings (p < 0.05) as did laboratories accessioning <150 samples per year. CONCLUSION: MGT is provided under widely varying conditions and regulatory frameworks. The data provided here may be a useful guide for policy action at both governmental and professional levels.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".