Considerations for the development of a reference method for sequencing of haploid DNA – an opinion paper on behalf of the IFCC Committee on Molecular Diagnostics. International Federation of Clinical Chemistry and Laboratory Medicine
Bibliographic record
Abstract
Following the completion of sequencing of the human genome, there has been a very rapid increase in the development of new molecular diagnostic tests. However, the numerous genetic tests and genetic testing technologies offered do not always satisfy essential quality criteria required to ensure confidence in the results that are produced. This is of particular importance for genetic tests since many patients may be tested for a particular genetic defect only once in their lifetime. Thus, there is a pressing need for comprehensive guidelines for the validation of molecular diagnostic tests and procedures, including DNA sequencing, the latter being a fundamental aspect of the development and validation of most genetic tests. To that end, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee for Molecular Diagnostics has prepared the following paper that describes a possible approach to the development of a reference method for sequencing of haploid DNA. We discuss various aspects which should be considered before, during and after applying the sequencing procedure, in order to achieve results with a known level of confidence, including robustness and assessments of quality.
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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 | 0.098 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".