Self-Reported Sexual Function Measures Administered to Female Cancer Patients: A Systematic Review, 2008–2014
Bibliographic record
Abstract
A systematic review was conducted to identify and characterize self-reported sexual function (SF) measures administered to women with a history of cancer. Using 2009 PRISMA guidelines, we searched electronic bibliographic databases for quantitative studies published January 2008-September 2014 that used a self-reported measure of SF, or a quality of life (QOL) measure that contained at least 1 item pertaining to SF. Of 1,487 articles initially identified, 171 were retained. The studies originated in 36 different countries with 23% from US-based authors. Most studies focused on women treated for breast, gynecologic, or colorectal cancer. About 70% of the articles examined SF as the primary focus; the remaining examined QOL, menopausal symptoms, or compared treatment modalities. We identified 37 measures that assessed at least one domain of SF, eight of which were dedicated SF measures developed with cancer patients. Almost one third of the studies used EORTC QLQ modules to assess SF, and another third used the Female Sexual Function Inventory. There were few commonalities among studies, though nearly all demonstrated worse SF after cancer treatment or compared to healthy controls. QOL measures are better suited to screening while dedicated SF questionnaires provide data for more in depth assessment. This systematic review will assist oncology clinicians and researchers in their selection of measures of SF and encourage integration of this quality of life domain in patient care.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".