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Record W3185979054 · doi:10.26443/mjm.v20i1.340

Approach to Gynecological Adnexal Masses

2021· article· en· W3185979054 on OpenAlexaffvenue
Laurie-Rose Dubé

Bibliographic record

VenueMcGill Journal of Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineMalignancyMedical diagnosisAdnexal massDifferential diagnosisGynecologyRadiologyGynecological ExaminationAbscessPhysical examinationClinical historyMedical historyGeneral surgeryPathologySurgery

Abstract

fetched live from OpenAlex

Gynecological pelvic masses are a common occurrence in women of all ages. The differential diagnosis is extensive and includes masses of all anatomical components of the female reproductive tract. This simple and refined approach leads the reader through the process of narrowing said differential. A thorough history and physical examination are essential steps that can hint to the appropriate investigations such as reproductive hormone levels, serum cancer biomarkers and imaging. Emphasis is put on ultrasound findings, helping differentiate not only diagnoses, but also the benign or malignant character of the mass. It also highlights the Risk of Malignancy Index I, commonly used in clinical practice to assess the risk of malignancy of a mass. Beyond the initial approach, some diagnoses and their management are discussed, from the very common functional cyst to the worrisome ovarian neoplasm, and mentioning more peculiar findings like tubo-ovarian abscess and leiomyoma.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.004

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 head0.065
GPT teacher head0.326
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes2
Has abstractyes

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