Cognitive impairment in the first year after breast cancer diagnosis: A prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to assess the relation between cancer treatments and incident cognitive impairment in breast cancer patients, taking into account the levels of anxiety before treatment. MATERIALS AND METHODS: We conducted a prospective cohort study with 418 newly diagnosed breast cancer patients with no cognitive impairment, defined as values at least 1.5 standard deviations below age- and education-adjusted cut-offs in the Montreal Cognitive Assessment (MoCA), at baseline. The Hospital Anxiety and Depression Scale and MoCA were used for evaluations before treatment and at 1-year after diagnosis. We used Poisson regressions to compute adjusted relative risks (RR) and corresponding 95% confidence intervals (95%CI) to identify predictors of cognitive impairment. RESULTS: The median (Percentile 25, Percentile 75) MoCA score before treatment was 24 (21, 26). A total of 8.1% (95%CI: 5.8, 11.2) of the patients presented incident cognitive impairment during the follow-up. There was a statistically significant interaction between anxiety at baseline and the effect of chemotherapy on the incidence of cognitive impairment (P for interaction = 0.028). There was a significantly increased risk of incident cognitive impairment among patients with no anxiety prior to treatment with schemes including doxorubicin and cyclophosphamide (adjusted RR = 4.22, 95%CI: 1.22, 14.65). CONCLUSION: There was a statistically significant association between chemotherapy and cognitive impairment, but only among women with no anxiety at baseline.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".