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Record W2131345840 · doi:10.3747/co.20.1358

Oncofertility in Canada: The Impact of Cancer on Fertility

2013· article· en· W2131345840 on OpenAlexafffundvenueabout
Ruth Ronn, Hananel Holzer

Bibliographic record

VenueCurrent Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcGill University Health CentreMcGill UniversityQueen's University
FundersHealth CanadaPediatric Oncology Group of OntarioMcMaster UniversityPartenariat Canadien Contre Le CancerMcGill UniversityIsrael Cancer Research Fund
KeywordsFertility preservationOncofertilityFertilityMedicineCancerMalignancyYoung adultRadiation therapyGynecologyGerontologyPopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer can be a devastating diagnosis. In particular, malignancy and its indicated treatments have profoundly negative effects on the fertility of young cancer patients. Oncofertility has emerged as a new interdisciplinary field to address the issue of gonadotoxicity associated with cancer therapies and to facilitate fertility preservation. In Canada, these fertility issues are often inadequately addressed despite the availability of resources. The goal of this four-part series is to facilitate systemic improvements in fertility preservation for adolescent and young adult Canadians with a new diagnosis of cancer. METHODS: In this article, we review the gonadotoxic effects of cancer treatment on young men and women of reproductive age. RESULTS: The detrimental effects of cancer on fertility can be severe and may vary depending on the chemotherapy, radiotherapy, or surgical treatments involved. CONCLUSIONS: Fertility preservation should be addressed in an effort to mitigate the gonadal damage that may come with cancer therapy.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.111
GPT teacher head0.436
Teacher spread0.325 · 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 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

Citations24
Published2013
Admission routes4
Has abstractyes

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