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Record W4220727950 · doi:10.1016/s2214-109x(22)00030-4

The iBreastExam versus clinical breast examination for breast evaluation in high risk and symptomatic Nigerian women: a prospective study

2022· article· en· W4220727950 on OpenAlexaff

Bibliographic record

VenueThe Lancet Global Health · 2022
Typearticle
Languageen
FieldMedicine
TopicBreast Lesions and Carcinomas
Canadian institutionsDalhousie University
FundersNational Cancer InstituteNational Institutes of HealthPfizerPrevent Cancer FoundationU.S. Department of Health and Human Services
KeywordsProspective cohort studyBreast cancerCancerCenter (category theory)MEDLINEPhysical examination

Abstract

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BACKGROUND: The iBreastExam electronically palpates the breast to identify possible abnormalities. We assessed the iBreastExam performance compared with clinical breast examination for breast lesion detection in high risk and symptomatic Nigerian women. METHODS: This prospective study was done at the Obafemi Awolowo University Teaching Hospital Complex (OAUTHC) in Nigeria. Participants were Nigerian women aged 40 years or older who were symptomatic and presented with breast cancer symptoms or those at high risk with a first-degree relative who had a history of breast cancer. Participants underwent four breast examinations: clinical breast examination (by an experienced surgeon), the iBreastExam (performed by recent nursing school graduates, who finished nursing school within the previous year), ultrasound, and mammography. Sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the iBreastExam and clinical breast examination for detecting any breast lesion and suspicious breast lesions were calculated, using mammography and ultrasound as the reference standard. FINDINGS: Between June 19 and Dec 5, 2019, 424 Nigerian women were enrolled (151 [36%] at high risk of breast cancer and 273 [64%] symptomatic women). The median age of participants was 46 years (IQR 42-52). 419 (99%) women had a breast imaging-reporting and data system (BI-RADS) assessment and were included in the analysis. For any breast finding, the iBreastExam showed significantly better sensitivity than clinical breast examination (63%, 95% CI 57-69 vs 31%, 25-37; p<0·0001), and clinical breast examination showed significantly better specificity (94%, 90-97 vs 59%, 52-66; p<0·0001). For suspicious breast findings, the iBreastExam showed similar sensitivity to clinical breast examination (86%, 95% CI 70-95 vs 83%, 67-94; p=0·65), and clinical breast examination showed significantly better specificity (50%, 45-55 vs 86%, 83-90; p<0·0001). The iBreastExam and clinical breast examination showed similar NPVs for any breast finding (56%, 49-63 vs 52%, 46-57; p=0·080) and suspicious findings (98%, 94-99 vs 98%, 96-99; p=0·42), whereas the PPV was significantly higher for clinical breast examination in any breast finding (87%, 77-93 vs 66%, 59-72; p<0·0001) and suspicious findings (37%, 26-48 vs 14%, 10-19; p=0·0020). Of 15 biopsy-confirmed cancers, clinical breast examination and the iBreastExam detected an ipsilateral breast abnormality in 13 (87%) women and missed the same two cancers (both <2 cm). INTERPRETATION: The iBreastExam by nurses showed a high sensitivity and NPV, but lower specificity than surgeon's clinical breast examination for identifying suspicious breast lesions. In locations with few experienced practitioners, the iBreastExam might provide a high sensitivity breast evaluation tool. Further research into improved specificity with device updates and cost feasibility in low-resource settings is warranted. FUNDING: Prevent Cancer Foundation Global Community Grant Award with additional support from the P30 Cancer Center Support Grant (P30 CA008748).

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)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.385
Teacher spread0.342 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

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