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Record W4206119078 · doi:10.15406/ogij.2020.11.00506

Sperm count and future challenges for cancer patients

2020· article· en· W4206119078 on OpenAlexaff
Murid Javed

Bibliographic record

VenueObstetrics & Gynecology International Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsOttawa Fertility Centre
Fundersnot available
KeywordsFertility preservationFertilitySpermSperm bankCancerTesticular cancerMedicineOocyte cryopreservationGynecologyBiologyAndrologyInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

Cancer has significantly harmful effects on sperm count, motility and sexual life of the survivor, thereby adversely affecting the fertility and post cancer quality of life. Use of safer chemotherapeutic agents, protection from radiation damage, cryopreservation of sperm and testicular tissue and use of protective drugs to reduce testicular damage is recommended. More research is needed to safeguard fertility of cancer affected children as testicular tissue cryopreservation is the only fertility preservation option. Advanced understanding of in vitro sperm production is needed. In this era of advanced assisted reproduction, the minimum requirement for fertilization is one healthy sperm for one oocyte. Children born after chemotherapy do not have statistically significant increase in malignant neoplasms. Proper cancer counseling and referral for fertility preservation are of high importance to protect fertility. The focus of this review is to share knowledge of sperm formation, importance of sperm count, nature of damage to male fertility, remedies to overcome damage and to improve post cancer quality of life of male cancer survivors.

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)0.001
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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