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Record W2910774598 · doi:10.1097/spc.0000000000000413

Cancer and fertility: optimizing communication between patients and healthcare providers

2019· review· en· W2910774598 on OpenAlexaff
Shiyang Shen, Phyllis Zelkowitz, Zeev Rosberger

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2019
Typereview
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsFertilityFertility preservationOpenness to experienceContext (archaeology)MedicineOncofertilityFamily medicineAffect (linguistics)Health careGynecologyEnvironmental healthPsychologyPopulationSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article reviews the status of guidelines and recommendations for communication between patients with cancer and healthcare providers (HCPs) concerning fertility issues. RECENT FINDINGS: The timing, the type of information provided, and the openness of HCPs can all affect how patients with cancer perceive discussions regarding fertility concerns and preservation. In addition, whether such discussions occur is associated with intrinsic factors, such as age and sex of the patients as well as HCP's knowledge level. It has also been found that the patients have different needs for information regarding fertility preservation and preferences for types of communication strategies regarding the impact of their disease and treatments on options for family planning. SUMMARY: Although discussions about fertility concerns in the context of cancer between physicians and patients are occurring more frequently, there are inconsistent findings regarding satisfaction with these discussions. Recent research has found that the timing, type of information given, and level of openness of the HCP can impact how patients perceive communications regarding the risks of cancer treatment on fertility preservation options and future family planning. Age, sex, and HCP's knowledge of fertility risks and fertility preservation services are also notable factors associated with whether and how extensively discussions about fertility take place. More women than men report having a fertility discussion with an HCP. However, men are more likely to report satisfaction with the fertility discussion than women.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.468
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.252
GPT teacher head0.473
Teacher spread0.220 · 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 teacher head, not a consensus.

Study designObservational
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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

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