Prevalence of cognitive impairment before treatments for prostate cancer
Bibliographic record
Abstract
Abstract Background/Objective Up to 30% of patients with cancer may present cognitive impairment (CI) before treatment but data are scarce regarding prostate cancer (PCa). We aim to estimate the prevalence of CI in patients with PCa, before cancer treatment. Methods Between February 2018 and April 2021, the NEON-PC cohort included 609 patients with a recent PCa diagnosis to be treated at Instituto Português de Oncologia do Porto. Previous history of chemotherapy, radiotherapy, androgen deprivation therapy, and neurologic or psychyatric conditions impairing cognitive performance were exclusion criteria. The Montreal Cognitive Assessment (MoCA) was used to assess cognitive performance before any treatment for PCa. Participants with a MoCA at least 1.5 SD below age- and education-specific norms were considered to have probable cognitive impairment (PCI) and were proposed for a comprehensive neuropsychological (NP) assessment. Participants scoring <2.0 SD age-corrected norms in at least one cognitive test, or < 1.5 SD age-corrected norms in ≥ 2 cognitive tests were classified as having CI. Data from the population-based cohort EPIPorto (n = 351 men, evaluated in 2013-2015) were used for comparison. Results/Discussion Prevalence of PCI was 17.4% in the EPIPorto and 15.1% in the NEON-PC cohort (age- and education-adjusted OR = 1.02,95% confidence interval: 0.70,1.50). NP assessment was performed in 65 patients with PCa: 38.5% had normal cognitive function; 7.7% had a light deficit (<1.0 SD of age-corrected norms in ≥ 1 cognitive tasks); and 53.9% had CI. Conclusions PCI was as frequent in patients recently diagnosed with PCa as in the general population. Prevalence of CI was lower than in previous reports, which may be explained by differences in the assessment and definition of CI and of the type of cancer. Funding POCI-01-0145FEDER-032358;PTDC/SAU-EPI/32358/2017;UIDB/4750/2020; SFRH/BD/119390/2016 Key messages Patients with prostate cancer and the general population had similar odds of having a score below normative values in the MoCA. Differences in the prevalence of CI between this study and others suggest that the type of cancer may affect patients’ cognitive performance differently, which deserves further confirmation.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".