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Record W2028642805 · doi:10.1016/j.breast.2012.07.017

Practice patterns and perceptions of margin status for breast conserving surgery for breast carcinoma: National Survey of Canadian General Surgeons

2012· article· en· W2028642805 on OpenAlexafffundabout
Peter Lovrics, Maggie Gordon, Sylvie D. Cornacchi, Forough Farrokhyar, Amanda Ramsaroop, Nicole Hodgson, May Lynn Quan, Francis Wright, Geoffrey A. Porter

Bibliographic record

VenueThe Breast · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsDalhousie UniversityHealth Sciences CentreFoothills Medical CentreSunnybrook Health Science CentreJuravinski Cancer CentreMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersCanadian Breast Cancer Research Alliance
KeywordsMedicineBreast cancerBreast-conserving surgeryMargin (machine learning)General surgerySurgeryCancerMastectomyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: We surveyed Canadian General Surgeons to examine decision-making in early stage breast cancer. METHODS: A modified Dillman Method was used for this mail survey of 1443 surgeons. Practice patterns and factors that influence management choices for: preoperative assessment, definition of margin status, surgical techniques and recommendations for re-excision were assessed. RESULTS: The response rate was 51% with 41% treating breast cancer. Most (80%) were community surgeons, with equal distribution of low/medium/high volume and years of practice categories. Approximately 25% of surgeons "sometimes or frequently" performed diagnostic excisional biopsies while 90% report "frequently" or "always" performing preoperative core biopsies. There was marked variation in defining negative and close margins, in the use of intra-operative margin assessment techniques and recommendations for re-excision. CONCLUSIONS: Responses revealed significant variation in attitudes and practices. These findings likely reflect an absence of consensus in the literature and potential gaps between best evidence and practice.

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 score0.086
Threshold uncertainty score0.845

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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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

Citations31
Published2012
Admission routes3
Has abstractyes

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