Trajectories of cognitive performance over 5 years in a cohort of breast cancer patients (NEON-BC)
Bibliographic record
Abstract
Abstract Purpose Cancer-related cognitive impairment may affect 30-70% of cancer patients, either at baseline or during and after treatment. We aimed to identify trajectories of cognitive performance, from before any treatment to 5 years later, in a cohort of breast cancer (BCa) patients. Methods BCa women admitted to the Portuguese Institute of Oncology, Porto, were included in the NEON-BC study during 2012. They were evaluated with the Montreal Cognitive Assessment (MoCA) before any treatment, and after 1, 3 and 5 years (N = 506, 503, 475 and 466, respectively). Nlme R package was used to fit a mixed-effect model of the trends in MoCA scores over time, with age and education as fixed effect. Coefficients of this model were retrieved to calculate an age- and education-modified MoCA score (mMoCA). Mclust was used to obtain clusters of trajectories of mMoCA. Results Two trajectories were identified, one with higher scores and increasing over time, and the other showing a continuous decline (25.9% of the participants and 84% of the women with cognitive impairment confirmed by neuropsychological tests and clinical examination by neurologist at the 5 year follow-up). Each trajectory was split into 2, according to scoring above or below to the median value of mMoCA at baseline to account for the possibility of patients being in a declining pathway before treatment. In addition to trajectories characterized by the highest and lowest scores, respectively, relatively stable over time, two trajectories with middle-range scores were observed, one increasing over time and the other decreasing (12.7% of the participants); being older than 65 years, suffering from anxiety, depression or poor sleep after treatment were more frequent among the latter. Conclusions One quarter of the 5-year breast cancer survivors had a declining trajectory in cognitive performance. Anxiety, depression and sleep quality should be considered as targets for preventive or curative interventions of cognitive decline. Key messages Cognitive decline occurs during breast cancer care, affecting one quarter of the patients. Anxiety, depression and sleep quality should be considered as targets for preventive or curative interventions of cognitive decline.
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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 | 0.001 | 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".