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Vaginal Radical Trachelectomy in the Treatment of Cervical Cancer: The Role of Frozen Section

2004· article· en· W2075134347 on OpenAlexaff
Caroline Tanguay, Marie Plante, Marie‐Claude Renaud, Michel Roy, Bernard T tu

Bibliographic record

VenueInternational Journal of Gynecological Pathology · 2004
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsHôtel-Dieu de Québec
Fundersnot available
KeywordsFrozen section procedureRadical HysterectomyMedicineTrachelectomyCervixCervical cancerHysterectomyStage (stratigraphy)LesionCervical conizationSurgeryCarcinomaRadiologyCancerCervical intraepithelial neoplasiaPathologyBiology

Abstract

fetched live from OpenAlex

Vaginal radical trachelectomy (VRT) is a new, alternative surgical procedure to radical hysterectomy for early stage invasive cervical carcinoma in women who desire to preserve fertility. The specimen includes the cervix, parametria, and the vaginal cuff. This study was designed to determine the indications and the best method for evaluating the resection margins of VRT specimens intraoperatively by frozen-section examination. We reviewed 61 VRT specimens planned between October 1991 and January 2002 in our center. A complementary radical hysterectomy is recommended when the tumor extends to within <5 mm of the margin. Of 61 patients, 56 were eligible (5 excluded; 53 VRT and 3 VRT followed by hysterectomy). Of 56 cases, 17 had no macroscopic or microscopic residual tumor. Of 27 cases with a nonspecific macroscopic lesion, more than one-half had no residual microscopic tumor, and the others had minimally (<1 mm) invasive residual carcinoma. In the remaining two cases with a macroscopic tumor, a longitudinal rather than a transverse frozen section was preferred, because it allowed the evaluation of the distance between the tumor and the endocervical margin. We recommend a frozen section, using a longitudinal section, only in those VRT specimens with a grossly visible lesion.

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 score0.214
Threshold uncertainty score0.366

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 head0.021
GPT teacher head0.321
Teacher spread0.300 · 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

Citations39
Published2004
Admission routes1
Has abstractyes

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