Investigating SSc Populations with Breast Cancer by Age and Antibody Presence
Bibliographic record
Abstract
Systemic sclerosis (Scleroderma, SSc) is a connective tissue disease that affects multiple organ systems in the body. Evidence suggests SSc has been linked to malignancies such as breast cancer (BC). This systematic review characterizes the clinical links of patients diagnosed with both SSc and BD, with a focus on antibody markers such as anti-centromere antibody (ACA), anti-nuclear antibody (ANA), and anti-topoisomerase antibody (anti-scl-70+), and relevant demographic factors such as age at SSc and BC diagnosis. A comprehensive literature search was conducted in PubMed using relevant keywords and MeSH terms. Inclusion criteria included English language retrospective analysis that characterized patients with SSc with, or without BC. Two independent reviewers will assess study eligibility based on predetermined criteria. Data extraction will include patient antibody measurements, demographics (age, gender), family history, social behaviors (alcohol use, smoking history), concurrent conditions, treatments, and adverse effects following treatment. Thirteen articles were identified in the literature with relevant data of SSc and BC patients. Studies encompassed research pertaining to SSc patients with, or without BC, and relevant risk factors being measured. Adverse treatment outcomes and concurrent conditions of BC were found when patients had a family history of SSc, BC, alcohol use, or smoking history. Our results suggest that the presence of ANA, ACA, or ATA in SSc patients is correlated with BC, and that SSc diagnosis commonly preceded BC diagnosis. However, further research is necessary to advance the linkage between SSc and BC, and whether occurrence of one condition is linked to the other.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".