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Performance characteristics of brief family history questionnaire to screen for Lynch syndrome in women with newly diagnosed ovarian cancers.

2021· article· en· W3168933935 on OpenAlexaffabout
Rachel Soyoun Kim, Alicia Tone, Raymond H. Kim, Matthew Cesari, Blaise Clarke, Lua Eiriksson, Hart Tae, Alice Lytwyn, Manjula Maganti, Steven Gallinger, Marcus Q. Bernardini, Amit M. Oza, Bojana Djordjevic, Jordan Lerner‐Ellis, Emily Van de Laar, Danielle Vicus, Trevor J. Pugh, Aaron Pollett, Sarah E. Ferguson

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsMcMaster UniversityMount Sinai HospitalHealth Sciences CentreOvarian Cancer CanadaSunnybrook Health Science CentreSinai Health SystemPrincess Margaret Cancer CentreToronto Metropolitan UniversityUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineFamily historyLynch syndromeOvarian cancerGynecologyInternal medicineReferralPopulationCohortSerous fluidOncologyCancerEndometrial cancerProspective cohort studyFamily medicineColorectal cancerDNA mismatch repair

Abstract

fetched live from OpenAlex

e22525 Background: Ovarian cancer (OC) is the third most common Lynch syndrome (LS)-associated cancer in women but there is no established screening strategy to identify LS in this population. An adequate family history may identify patients suspected of LS, prompting a referral to genetic assessment. We have previously validated the 4-item brief Family History Questionnaire (bFHQ) in endometrial cancers. The objective of this study was to assess whether bFHQ can be used as a screening tool to identify women with OC at risk of LS. Methods: Prospective cohort study recruited women with newly diagnosed non-serous/non-mucinous OC from three cancer centers in Ontario, Canada. Participants completed bHFQ, extended Family History Questionnaire (eFHQ; encompassing Amsterdam II criteria, Society of Gynecologic Oncology 20-25% criteria and Ontario Ministry of Health criteria), immunohistochemistry (IHC) for mismatch repair (MMR) proteins and universal germline testing for LS. The performance characteristics were compared between bFHQ, eFHQ, and IHC. Results: Of 215 participants, 169 (79%) were evaluable with both bFHQ and germline mutation status; 12 of these 169 were confirmed to have LS (7%). Nine of 12 patients (75%) with LS were correctly identified by bFHQ, compared to 6 of 11 (55%) by eFHQ and 11 of 13 (85%) by IHC. The sensitivity, specificity, positive predictive values and negative predictive values of bFHQ were 75%, 66%, 15% and 98%, compared to 55%, 92%, 35% and 96% for eFHQ and 85%, 90%, 39% and 99% for IHC respectively. IHC was the most sensitive and specific approach. The 4-item bFHQ was more sensitive than eFHQ and took less than 10 minutes for each patient to complete. Conclusions: Patient-administered bFHQ may serve as an adequate screening tool to triage women with OC for further genetic assessment for LS, especially in centers without access to universal tumor testing for IHC for MMR.[Table: see text]

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)0.002
metaresearch head score (Gemma)0.006
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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.063
GPT teacher head0.371
Teacher spread0.308 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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