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Record W2018978133 · doi:10.1097/coc.0b013e31819fdfc8

Adenoid Cystic Breast Carcinoma

2010· article· en· W2018978133 on OpenAlexaffabout
Nicole C. Hodgson, Alice Lytwyn, Sarah Bacopulos, Leela Elavathil

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineLumpectomyAdenoid cystic carcinomaMastectomySurgical marginRadiologyMalignancyLymph nodeBreast cancerAxillary lymph nodesSurgeryCarcinomaCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Adenoid cystic carcinoma of the breast (ACCB) is a rare malignancy with favorable prognosis: axillary lymph node involvement, distant metastases, and death due to disease are uncommon. ACCB may recur locally many years after primary surgical excision and may be substantially higher if primary procedure is lumpectomy rather than mastectomy. METHODS: Pathology database searched to identify patients diagnosed with ACCB between 1988 and 2007 at Hamilton Health Sciences Centre, Hamilton, Ontario, Canada.Two pathologists independently reviewed histology to confirm diagnosis of ACCB, and documented surgical procedure, tumor size, tumor grade, surgical margin, and lymph node status. Immunohistochemistry was performed on representative blocks and independently reviewed by 2 pathologists. Clinical and radiologic data were retrospectively reviewed. RESULTS: Fifteen cases of ACCB were identified and pathology slides were available for 12. The median age was 62 years. Seven patients presented with a palpable mass and breast pain was described in 3. Positive surgical margins were identified in 5 patients (42%). Only 3 patients had postoperative radiation therapy. CONCLUSIONS: Our series shows frequent resection margin involvement in ACCB. Neither clinical nor mammographic examination consistently delineated full tumor extent preoperatively. Future use of magnetic resonance imaging in preoperative assessment may prevent high positive margin rate when lumpectomy is planned. Histologic assessment of tumor extent may be difficult, but immunohistochemistry may be helpful in this regard.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.041
GPT teacher head0.424
Teacher spread0.383 · 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 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

Citations29
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Clinical OncologySame topicSalivary Gland Tumors Diagnosis and TreatmentFrench-language works237,207