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Record W4220783756 · doi:10.14740/jcs453

Juvenile Atypical Ductal Hyperplasia: A Case Report

2022· article· en· W4220783756 on OpenAlexvenueno aff
Sindi Diko, Lisa O’Kane, Nadra Moulayes

Bibliographic record

VenueJournal of Current Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicBreast Lesions and Carcinomas
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerFibroadenomaPopulationDiseaseCancerYoung adultJuvenileGynecologyPediatricsPathologyInternal medicine

Abstract

fetched live from OpenAlex

Breast masses, benign and malignant, are extremely common in women. Benign breast masses have a variety of risk factors and are usually most common in women aged 30 - 40 years. Although much research has been conducted on benign breast disease and breast cancer in adult women, there remains a paucity of data on breast masses in adolescent women. More specifically, there is very little evidence regarding atypical ductal hyperplasia (ADH) in young women and the future risks it may carry as it is known to have a 4 to 5-fold increase for breast cancer in the adult population. We present a case of an 18-year-old female with ADH involving a fibroadenoma, with the hopes of highlighting the unique concerns and questions this diagnosis may bring to the young female population. Our patient’s questions centered around the risk this diagnosis carries of future cancer and what steps should be taken to minimize that risk. Although many models and guidelines exist to answer these questions in the older female, there is no consensus about treatment and monitoring ADH in a juvenile. By reviewing this case, we emphasize the need for future studies to quantify the risk of cancer progression from ADH in young females. We also demonstrate the need for guidelines to monitor and treat these findings in our younger populations. J Curr Surg. 2022;12(1):21-23 doi: https://doi.org/10.14740/jcs453

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.308
Teacher spread0.258 · 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.

Study designCase report
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

Citations1
Published2022
Admission routes1
Has abstractyes

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