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Record W4392589424 · doi:10.1016/j.gimo.2024.101781

P867: “I worry I don’t have control”: The psychosocial impacts of living with a hereditary cancer syndrome

2024· article· en· W4392589424 on OpenAlexaff
Jordan Sam, Brooklyn Sparkes, Marc Clausen, Carly Butkowsky, Emma Reble, Sepideh Rajeziesfahani, Ridhi Gopalakrishnan, Vernie Aguda, Melyssa Aronson, Derrick Bishop, Lesa Dawson, Andrea Eisen, Tracy Graham, Jane Green, Chloe Mighton, Julee Pauling, Claudia Pavao, Petros Pechlivanoglou, Catriona Remocker, Sevtap Savas, Sophie Sun, Teresa Tiano, Angelina Tilley, Kevin E. Thorpe, Kasmintan A. Schrader, Yvonne Bombard, Holly Etchegary

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsHospital for Sick ChildrenHealth Sciences CentreSunnybrook Health Science CentreSinai Health SystemUniversity of TorontoMemorial University of NewfoundlandSt. Michael's Hospital
FundersSchool of Medicine, University of MissouriSchool of Medicine, University of KansasChildren's Mercy HospitalUniversity of Missouri
KeywordsWorryPsychosocialHereditary CancerMedicineCancerPsychologyGerontologyPsychiatryInternal medicineAnxiety

Abstract

fetched live from OpenAlex

Hereditary cancer syndromes (HCS) are one of the most common forms of inherited diseases, accounting for up to 10% of cancers. Hereditary Breast and Ovarian Cancer Syndrome (HBOC) and Lynch Syndrome (LS) are the most prevalent types of HCS. Patients with HCS are genetically more susceptible to developing cancer in their lifetime and often require consistent, lifelong screening and monitoring. Various facets of patients’ lives may be indirectly affected by their diagnosis and care. However, limited evidence describes the range of psychosocial and lifestyle impacts of HCS following a positive genetic 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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.334
Teacher spread0.318 · 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
Published2024
Admission routes1
Has abstractyes

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