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Record W3080411100

Family History: Effectiveness in Identifying Families at High Risk for Pediatric Onset Cancer Predisposition Syndromes

2020· article· en· W3080411100 on OpenAlexaboutno aff
Christina Fujii

Bibliographic record

VenueeScholarship (California Digital Library) · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsFamily historyReferralMedicineCancerFamily medicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Family history is an important screening tool that can highlight features suggestive of a cancer predisposition syndrome (CPS). In collaboration with the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) project through McGill University and the Genome 4 Kids (G4K) study through St. Jude Children’s Research Hospital, a retrospective analysis of an existing data set of pediatric oncology patients compared aspects of family cancer histories in participants with and without a CPS. MIPOGG is an app that generates a recommendation for or against a genetics referral based on the presence or absence of personal and family history features associated with a high risk for a CPS. Analysis of the features in MIPOGG indicated that personal history features alone were significantly associated with identifying a CPS in participants while family history features alone were not. Although the yield of identifying participants with a CPS using family history features was low, one participant with a CPS was only classified as high-risk for a CPS due to a family history feature. Factors such as a patient’s age and cancer type did not have any clear associations with the degree of relationship or ages of relatives with cancers in a family history. This study highlighted the importance of detailed characterization of personal history features and the low yield of family history as a screening tool for CPSs in the pediatric oncology setting. However, an important subset of pediatric oncology patients with a CPS will only have features concerning for a CPS in their family history; if only personal history features are evaluated, patients such as these may be missed as being at high risk for a CPS. Recognizing the power and limitations of family history as a screening tool for CPS identification can aid in the effectiveness of a healthcare provider’s risk assessment for a CPS at the time of a child’s cancer diagnosis.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.065
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.033
GPT teacher head0.265
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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