Intraindividual variability in reaction time before and after neoadjuvant chemotherapy in women diagnosed with breast cancer
Bibliographic record
Abstract
OBJECTIVE: Women treated with chemotherapy for breast cancer experience subtle cognitive deficits. Research has focused on mean performance level, yet recent work suggests that within-person variability in reaction time performance may underlie cognitive symptoms. We examined intraindividual variability (IIV) in women diagnosed with breast cancer and treated with neoadjuvant chemotherapy. METHODS: Patients (n = 28) were assessed at baseline before chemotherapy (T1), approximately 1 month after chemotherapy but prior to surgery (T2), and after surgery about 9 months post chemotherapy (T3). Healthy women of similar age and education (n = 20) were assessed at comparable time intervals. Using a standardized regression-based approach, we examined changes in mean performance level and IIV (eg, intraindividual standard deviation) on a Stroop task and self-report measures of cognitive function from T1 to T2 and T1 to T3. RESULTS: At T1, women with breast cancer were more variable than controls as task complexity increased. Change scores from T1 to T2 were similar between groups on all Stroop performance measures. From T1 to T3, controls improved more than women with breast cancer. IIV was more sensitive than mean reaction time in capturing group differences. Additional analyses showed increased cognitive symptoms reported by women with breast cancer from T1 to T3. Specifically, change in language symptoms was positively correlated with change in variability. CONCLUSIONS: Women with breast cancer declined in attention and inhibitory control relative to pretreatment performance. Future studies should include measures of variability, because they are an important sensitive indicator of change in cognitive function.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".