Defining and Reporting on Critical Values in Genetics: A Laboratory Survey
Bibliographic record
Abstract
BACKGROUND: Although an obvious critical value in metabolic genetics would be ammonia, it is more challenging to define critical values in molecular genetics and cytogenetics. The objective of this study was to survey genetic laboratories in Ontario, Canada, to determine whether different centers considered similar results as critical and thus potentially deserving of a different reporting process. METHODS: An online 11-question survey was emailed to Ontario laboratory directors, and the results were analyzed. RESULTS: The response rate was 82% (9/11). Cytogenetics and molecular genetics services were each provided by 7 of the 9 centers, with 3 centers providing biochemical/metabolic genetics services and 1 providing maternal marker serum screening services. The case type (e.g., prenatal, newborn, or expedited by the ordering physician) was one factor. Quantitative fluorescence PCR for autosomal aneuploidy, pathogenic variants in both prenatal and postnatal settings, and oncological results were considered critical cytogenetics results. Pathogenic prenatal cases, indeterminate results, and unexpected results were considered more critical for molecular genetics. Critical results were more likely to prompt a telephone call or email to the ordering physician. CONCLUSION: Ontario genetics laboratories tended to have similar reporting processes for critical results. Both the types of cases and the pathogenicity of the result define what values are considered critical.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".