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Record W2575432205 · doi:10.1002/jso.24520

Preoperative MRI of the breast and ipsilateral breast tumor recurrence: Long‐term follow up

2017· article· en· W2575432205 on OpenAlexaff
Mai‐Kim Gervais, Ellen Maki, Dan Schiller, Pavel Crystal, David R. McCready

Bibliographic record

VenueJournal of Surgical Oncology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity of AlbertaUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineBreast-conserving surgeryBreast cancerBreast MRIRetrospective cohort studyInternal medicineRadiologyMastectomyCancerMammography

Abstract

fetched live from OpenAlex

BACKGROUND: Local recurrence after breast conserving surgery is reported in 5-10% of cases. This study aims to determine if preoperative MRI is associated with reduced IBTR rates in the longer term and evaluate IBTR rates of a high risk (TN and Her-2 positive) subgroup in those receiving MRI or not. METHODS: Between 1999 and 2005, patients with invasive breast cancer undergoing BCS and radiation were identified. Primary endpoint was IBTR rate. RESULTS: The cohort consisted of 470 cases: 27% underwent MRI and 73% did not. Median follow-up was 97 months. Overall 10-year IBTR rate was 3.6%. There was no significant difference in IBTR rate at 10 years between those receiving MRI or not (1.6% vs. 4.2% (P = 0.37). The TN and Her-2 positive combined subgroup had a higher IBTR rate than all others (9.8% vs. 1.7%, P = 0.001). In the group without MRI, the IBTR rate of the high risk group was 11.8% compared to 1.8% in the remainder (P = 0.002). CONCLUSION: With 10-year follow-up, there was no significant difference in IBTR rate whether preoperative MRI is performed versus not. The high risk population showed an increased IBTR rate, this was more marked in those who did not receive MRI.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.359

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 studies0.0000.001
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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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