<i>CHEK2</i>-Positive Breast Cancers in Young Polish Women
Bibliographic record
Abstract
PURPOSE: To investigate the contribution of CHEK2 mutations to early-onset breast cancer in Poland and to establish the characteristic features of these cancers. EXPERIMENTAL DESIGN: We studied 3,228 women diagnosed with breast cancer under the age of 51 years and 5,496 population controls. CHEK2 mutations were detected by RFLP-PCR or allele-specific oligonucleotide-PCR assays. Clinical and pathologic features of CHEK2-positive cases and CHEK2-negative cases were compared. RESULTS: A truncating CHEK2 mutation (1100delC or IVS2+1G>A) was seen in 47 of 3,228 cases and in 34 of 5,496 controls (odds ratio, 2.4; P = 0.0001). The CHEK2 I157T missense mutation was present in 207 of 3,228 cases, compared with 264 of 5,496 controls (odds ratio, 1.4; P = 0.002). Breast cancers in women with a CHEK2 mutation were more commonly of lobular histology (21.5% versus 15.8%; P = 0.05), of size >2 cm (54.8% versus 43.5%; P = 0.01), or of multicentric origin (28.7% versus 19.5%; P = 0.01) than were cancers from women without a CHEK2 mutation. Bilateral cancers were equally common in both subgroups. CONCLUSION: Three founder alleles in CHEK2 contribute to early-onset breast cancer in Poland. Breast tumors which arise in carriers of CHEK2 mutations seem to be similar to those of breast cancers in the population at large.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".