Use of an oncology sperm bank: a Canadian experience
Bibliographic record
Abstract
Background: We performed a retrospective chart review in 2006to review oncology patients’ use of banked semen samples infertility treatments at a tertiary care centre.Methods: From 2002 to 2005, 367 oncology patients bankedsemen. During the same period, 31 patients used banked samplesin 48 treatment cycles. Samples were used for intrauterine insemination(IUI) in 28 cycles and for in vitro fertilization (IVF) withor without intracytoplasmic sperm injection (ICSI) in 20 cycles.Results: Pregnancy rates per cycle were 21% for IUI and 50%for IVF with or without ICSI. Overall, 16 of the 31 couples achieveda pregnancy with assisted reproductive technologies (52%).Conclusion: This data indicates high pregnancy success rates withthe use of banked semen samples from men with cancer.Contexte : Une revue rétrospective de dossiers a été effectuée afind’examiner le recours à des échantillons de sperme provenantde patients cancéreux pour le traitement de l’infertilité dans uncentre de soins tertiaires.Méthodes : Entre 2002 et 2005, des échantillons de spermeprovenant d’un total de 367 patients atteints de cancer ont été misen banque. Durant la même période, 31 patientes ont utilisé ceséchantillons au cours de 48 cycles de traitement. Les échantillonsont été utilisés pour insémination intra-utérine (IIU) lors de28 cycles et pour fertilisation in vitro (FIV) avec ou sans injectionintracytoplasmique de sperme (ICSI) lors de 20 cycles.Résultats : Le taux de grossesse par cycle était de 21 % avecl’IIU et de 50 % pour la FIV avec ou sans ICSI. Au total, 16 des31 patientes sont devenues enceintes (52 %).Conclusion : Ces données montrent des taux élevés de grossesseobtenus par l’utilisation d’échantillons de sperme provenantd’hommes atteints de cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".