Using a Family History Questionnaire to Identify Adult Patients with Increased Genetic Risk for Sarcoma
Bibliographic record
Abstract
BACKGROUND: Sarcomas in adults can be associated with hereditary cancer syndromes characterized by early-onset predisposition to numerous types of cancer. Because of variability in familial presentation and the largely unexplained genetic basis of sarcomas, ascertainment of patients for whom a genetics evaluation is most indicated poses challenges. We assessed the utility of a Sarcoma Clinic Genetic Screening (scgs) questionnaire in facilitating that task. METHODS: Between 2008 and 2012, 169 patients (median age: 53 years; range: 17-88 years) completed a self-administered scgs questionnaire. A retrospective chart review was completed for all respondents, and descriptive statistics were reported. Probands were divided into two groups depending on whether they did or did not report a family history of Li-Fraumeni syndrome-type cancers. RESULTS: A family history of cancer (as far as 3rd-degree relatives) was reported in 113 of 163 sarcoma patients (69%). Eeles Li-Fraumeni-like (lfl) criteria were fulfilled in 46 probands (28%), Chompret lfl in 21 (13%), Birch lfl in 8 (5%), and classic Li-Fraumeni in none. In the 10 probands tested for TP53 mutations, 1 pathogenic mutation was found. Further investigation of selected families led to the discovery of germline mutations in MLH1, MSH2, and APC genes in 3 individuals. CONCLUSIONS: The scgs questionnaire was useful for ascertaining probands with sarcoma who could benefit from a genetic assessment. The tool allowed us to identify high-risk families fitting the criteria for lfl and, surprisingly, other hereditary cancer syndromes. Similar questionnaires could be used in other cancer-specific clinics to increase awareness of the genetic component of these cancers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".