Bibliographic record
Abstract
The aim of this article is to selectively review the current research findings related to quality of life and prostate cancer. English-language journals indexed in MEDLINE, PubMed, and CINAHL published between 1999 and 2005 were searched for relevant articles using the following keywords: "quality of life and prostate cancer," "prostatectomy," "radiation therapy," "brachytherapy," "cryotherapy," or "androgen deprivation therapy." References in selected articles were reviewed for potentially relevant articles not identified through database searches. All treatment modalities have a significant impact on quality of life for men with local or advanced prostate cancer. Alterations in sexual functioning cause the most significant impact on quality of life for men. Quality of life is decreased in both the short and long term for men with prostate cancer. Oncology nurses must be cognizant of the challenges that a diagnosis of prostate cancer presents to the man with prostate cancer and his partner. Patients should be fully informed of the potential for impact on quality of life with all treatment modalities, and the oncology nurse can play an important role in both providing this information and supporting the patient when quality of life is impacted.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".