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Record W2066162330 · doi:10.2466/pr0.98.3.873-881

Using the Impact of Event Scale to Evaluate Distress in the Context of Genetic Testing for Breast Cancer Susceptibility

2006· article· en· W2066162330 on OpenAlexaff
Michel Dorval, Mélanie Drolet, Mélanie LeBlanc, Elizabeth Maunsell, Michel J. Dugas, Jacques Simard

Bibliographic record

VenuePsychological Reports · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsCanadian Apheresis GroupConcordia UniversityUniversité Laval
Fundersnot available
KeywordsContext (archaeology)Breast cancerDistressGenetic testingCancerPsychologyEvent (particle physics)MedicineOncologyInternal medicineDemographyClinical psychologyBiology

Abstract

fetched live from OpenAlex

The data obtained with two forms of the Impact of Event Scale were compared, one referring to a BRCA1/2 test result (IES-T) and another to cancer (IES-C). The sample consisted of 272 women with a family history suggestive of a BRCA1/2 mutation who underwent genetic testing and received results: noncarrier, carrier, or inconclusive. Globally, mean scores on the IES-C form were higher than those obtained on the IES-T form. Among carriers of a BRCA1/2 mutation, mean scores on the two forms were similar and agreement was good, as measured by the intraclass correlation coefficient (.83; 95% 95% CI=.72, .91). Agreement between the forms was poor to fair among noncarriers (ICC= .38; CI= .15, .57) and women with an inconclusive result (ICC= .40; 95% CI= .26, .52). Having had cancer increased total scores but had little influence on agreement between scores on forms. These findings highlight the importance of carefully selecting the form of the Impact of Event Scale in the context of genetic testing for breast cancer susceptibility.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.492
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.421
Teacher spread0.363 · 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 teacher head, 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

Citations4
Published2006
Admission routes1
Has abstractyes

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