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Record W2166549147 · doi:10.1093/humrep/dem084

Laparoscopy-guided myometrial biopsy in the definite diagnosis of diffuse adenomyosis

2007· article· en· W2166549147 on OpenAlexaboutno aff
Cherng Jye Jeng, Shu-Hsien Huang, Junyu Shen, Chih‐Wei Chou, Chii‐Ruey Tzeng

Bibliographic record

VenueHuman Reproduction · 2007
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsnot available
FundersNational Science Council
KeywordsAdenomyosisMedicineLaparoscopyBiopsyEndometrial biopsyProspective cohort studyPelvic painRadiologyLeiomyomaEndometriosisGynecologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to investigate the usefulness of laparoscopy-guided myometrial biopsy in the diagnosis of diffuse adenomyosis. METHODS: This prospective non-randomized study (Canadian Task Force classification II-1) was conducted in a tertiary medical center. One hundred patients who had clinical signs and symptoms strongly suggestive of adenomyosis were included as the study sample. Transvaginal sonography, serum CA-125 determination and laparoscopy-guided myometrial biopsy were performed. RESULTS: The mean largest myometrial thickness was 3.10+/-0.56 cm (range 2.30-4.50). The mean serum CA-125 level was 49.64+/-38.30 U/ml (range 10.90-205.28). Of these 100 patients, adenomyosis was pathologically proven in 92 patients, small leiomyoma in four patients and myometrial hypertrophy in four patients. The sensitivity of myometrial biopsy was 98% and the specificity 100%; the positive predictive value was 100% and the negative predictive value 80%, which were superior to those of transvaginal sonography, serum CA-125 determination or the combination of both. CONCLUSION: Laparoscopy-guided myometrial biopsy is a valuable tool for obtaining a definite diagnosis of diffuse adenomyosis with preservation of the uterus in infertility workup or in the evaluation of dysmenorrhea or chronic pelvic pain.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.132
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.088
GPT teacher head0.378
Teacher spread0.290 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

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