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Record W2906387696 · doi:10.32768/abc.201854159-162

Recurrent Multiple Fibroadenomas: History of a Case Presented in MDT Meeting With Clinical Discussion and Decision Making

2018· article· en· W2906387696 on OpenAlexaff
Negar Mashoori, Abdolali Assarian, Sanaz Zand, Érica Patocskai

Bibliographic record

VenueArchives of Breast Cancer · 2018
Typearticle
Languageen
FieldMedicine
TopicBreast Lesions and Carcinomas
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFibroadenomaMedicinePhysical examinationPresentation (obstetrics)Medical historyCase presentationGeneral surgeryOncoplastic SurgeryModalitiesIntervention (counseling)SurgeryBreast surgeryBreast cancerCancerInternal medicineNursing

Abstract

fetched live from OpenAlex

Background: Fibroadenoma is a common benign breast disorder in young women which has a low risk of malignant transformation. Most fibroadenomas present as a single mass, but the presence of multiple fibroadenomas can be seen in 15–20% of patients, with average number of 3–4 masses in one breast. In different studies and reports, various treatment modalities-including observation and follow up, surgery, radiofrequency ablation, etc- have been proposed, though the best management for these patients are not determined yet. Case presentation: We present the case of 33-year-old female with history of multiple bilateral benign breast lesions with a presumptive diagnosis of fibroadenomas. She had three previous surgical excisions in the past 14 years. Her case was presented to a breast MDT meeting to obtain a recommendation on appropriate management. Question: The proposed a question in MDT concerned the best and most appropriate management plan for the patient; Does she require further surgical excisions? And if not, how should she be followed? Conclusion: After reviewing past medical history, physical examination, and all documents regarding the patient, MDT members recommended that the patient should be managed with close follow up with physical examination and ultrasound every 6 months. The necessity of further surgical intervention would be determined according to any new findings.

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)0.000
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 score0.510
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.323
Teacher spread0.297 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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