Androgen-deprivation therapy and cognitive decline in the NEON-PC prospective study during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Androgen-deprivation therapy (ADT) has been associated with cognitive decline, but results are conflicting. This study describes changes in cognitive performance in patients with prostate cancer, according to ADT, during the first year after prostate cancer diagnosis. PATIENTS AND METHODS: Patients with prostate cancer treated at the Portuguese Institute of Oncology of Porto (n = 366) were evaluated with the Montreal Cognitive Assessment (MoCA), before treatment and after 1 year. All baseline evaluations were performed before the coronavirus disease 2019 (COVID-19) pandemic and 69.7% of the 1-year assessments were completed after the first lockdown. Cognitive decline was defined as the decrease in MoCA from baseline to the 1-year evaluation below 1.5 standard deviations of the distribution of changes in the whole cohort. Participants scoring below age- and education-specific normative reference values in the MoCA were considered to have cognitive impairment. Age- and education-adjusted odds ratios (aORs) were computed for the association between ADT and cognitive outcomes. RESULTS: Mean MoCA scores increased from baseline to the 1-year evaluation (22.3 versus 22.8, P < 0.001). Cognitive decline was more frequent in the ADT group, and even more after the onset of the COVID-19 pandemic (aOR 6.81 versus 1.93, P for interaction = 0.233). The 1-year cumulative incidence of cognitive impairment was 6.9% (9.1% before and 3.7% after the pandemic onset), which was higher among patients receiving ADT, but only after the pandemic (aOR 5.53 versus 0.49, P for interaction = 0.044). CONCLUSIONS: ADT was associated with worse cognitive performance of patients with prostate cancer, mostly among those evaluated after the first COVID-19 lockdown.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".