Prevalence of Cognitive Impairment before Prostate Cancer Treatment
Bibliographic record
Abstract
Cognitive impairment is common among patients with different types of cancer, even before cancer treatment, but no data were reported among patients with prostate cancer (PCa), who may be at high risk due to advanced age. This study aims to estimate the prevalence of cognitive impairment before PCa treatment. Between February 2018 and April 2021, the NEON-PC cohort recruited 605 patients with PCa proposed for treatment at the Portuguese Institute of Oncology of Porto. The Montreal Cognitive Assessment (MoCA) was used to assess cognitive performance. Participants with a MoCA < 1.5 standard deviations (SD) of age- and education-specific normative values were considered to have probable cognitive impairment (PCI) and were referred for a comprehensive neuropsychological assessment. Data from the population-based cohort EPIPorto (n = 351 men aged ≥40 years, evaluated in 2013−2015) were used for comparison. The prevalence of PCI was 17.4% in EPIPorto and 14.7% in NEON-PC (age- and education-adjusted odds ratio: 0.82, 95%CI: 0.58,1.18). Neuropsychological assessment was performed in 63 patients with PCa: 54.0% had cognitive impairment. These results suggest that the impact of PCa on cognitive performance could be negligible in the short term, contrary to what other studies have reported regarding other types of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".