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Record W4386106724 · doi:10.3802/jgo.2023.34.e88

Closing the gap for cervical cancer research in Vietnam: current perspectives and future opportunities: a report from the 5th Gynecologic Cancer InterGroup (GCIG) Cervical Cancer Research Network (CCRN) Education Symposium

2023· article· en· W4386106724 on OpenAlexaff
Ngoc Phan, Quy T. Tran, Nhan P. T. Nguyen, Hang Thu Nguyen, Linh D.N. Tran, Viet C. Pham, Katherine Bennett, Adriana Chávez-Blanco, Marie Plante, Dong Hoon Suh, Remi A. Nout, David S.P. Tan

Bibliographic record

VenueJournal of Gynecologic Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGynecologic cancerMedicineCervical cancerClosing (real estate)CancerOncologyInternal medicineOvarian cancer

Abstract

fetched live from OpenAlex

PURPOSE1. Review the incidence and the prevalence rates, and the current management strategies being used to reduce the cervical cancer in Vietnam.2. Review the current state of research capabilities in Vietnam regards to the infrastructure for radiation therapy (RT), surgical procedures, and systemic therapy for treating cervical cancer.3. Identifying the gaps in the infrastructure for cervical cancer treatment and research in Vietnam and discussing potential solutions for addressing these gaps.4. Discuss the perspectives of government involvement including regulation consideration on cervical cancer research and treatment in Vietnam. 5. Discuss the potential opportunities for improving cervical cancer research and treatment in Vietnam, including education, collaborations, and funding sources.

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)0.012
metaresearch head score (Gemma)0.009
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.193
GPT teacher head0.538
Teacher spread0.345 · 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
GenreOther

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

Citations1
Published2023
Admission routes1
Has abstractyes

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