Selected Abstracts Submitted to the Fourth International Symposium on Hereditary Breast and Ovarian Cancer
Bibliographic record
Abstract
Background: Nearly 15% of DNA tests for BRCA1/2 results in the identification of an unclassified variant (UV). In DNA diagnostic laboratories in The Netherlands, a 4-group classification system (class I to IV) is in use (Bell et al.). Aim of this study was to investigate whether the UVs in different classes showed a significant difference in their in silico characteristics and would justify current differences in protocols for counselling with respect to communication to the counselees. Methods: Missense UVs in BRCA1/2 identified between 2002 and 2010 (n = 88) were analyzed. In silico analysis of UVs was performed using SIFT– analysis Grantham score and AGVGD for the predicted severity of amino acid substitutions. Each UV was classified to one of the four classes. Results: More than half of the UVs (n = 50) were predicted to be tolerated using SIFT-analysis. Accordingly, all these variants are scored as neutral (C0) by AGVGD. Of the remaining 38 UVs not tolerated using SIFT-analysis, 19 were scored as C0 (neutral), 8 were scored C15–C25 (intermediate) and 11 were scored C35 or higher (likely to be pathogenic). Although class III UVs more frequently show in silico parameter outcomes that are suspicious for a pathogenic effect, the observed differences are not absolute. Seven UVs classified in class II had similar in silico profiles with 7 UVs in class III. Conclusion: This study showed that, in general, in silico analysis is consistently applied and proved to be able to discriminate between the different classes of UVs. However, additional analyses will be required to classify the UVs with more accuracy. In order to reduce psychological distress in families in which a UV is identified, we propose that communication of a UV should not primarily depend on its class, but also on the possibility to perform additional research in the family.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.242 | 0.057 |
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".