Leveraging the power of new molecular technologies in the clinical setting requires unprecedented awareness of limitations and drawbacks: experience of one diagnostic laboratory
Bibliographic record
Abstract
BACKGROUND: Advances in molecular technologies and in-silico variant prediction tools offer wide-ranging opportunities in diagnostic settings, yet they also present with significant limitations. OBJECTIVE: Here, we contextualise the limitations of next-generation sequencing (NGS), multiplex ligation-dependent probe amplification (MLPA) and in-silico prediction tools routinely used by diagnostic laboratories by reviewing specific experiences from our diagnostic laboratory. METHODS: We investigated discordant annotations and/or incorrect variant 'callings' in exons of 56 genes constituting our cardiomyopathy and connective tissue disorder NGS panels. Discordant variants and segmental duplications (SD) were queried using the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool and the University of California Santa Cruz genome browser, respectively, to identify regions of high homology. Discrepant variant analyses by in-silico models were re-evaluated using updated file entries. RESULTS: presented the highest risk (15.7%) of incorrect variant 'calling'. The inaccurate 'callings' and discrepant in-silico predictions were resolved following detailed investigation into the source of error. CONCLUSION: Recognising the limitations described here may help avoid incorrect diagnoses and leverage the power of new molecular technologies in diagnostic settings.
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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 | 0.064 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".