Effectiveness of Intraindividual Variability in Detecting Subtle Cognitive Performance Deficits in Breast Cancer Patients
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine if intraindividual variability would be more sensitive than speed or accuracy in detecting subtle cancer-related cognitive disturbance. METHODS: Data were from a previous study in which 60 breast cancer (BC) patients underwent neuropsychological assessment before commencement of chemotherapy and again following each chemotherapy cycle. Sixty healthy controls were tested at equivalent intervals. Hierarchical linear modeling was used to compare the BC and control groups in terms of accuracy, mean reaction time, and intraindividual variability in reaction time on a computerized continuous performance test with three conditions: a simple reaction time task, a "1-back" task, and a "2-back" task. RESULTS: An increase in accuracy and response speed over sessions was noted on some tasks in the sample as a whole but there were no differences in these parameters between the BC patients and the controls on any condition. There was a significant group difference in change in intraindividual variability across sessions (i.e., a "group × session interaction"), albeit only on the most complex "2-back" task. Intraindividual variability declined in the control group (i.e., consistency improved with practice) but this practice effect was significantly attenuated in the BC patients. There was no main effect of group on the "2-back" task. CONCLUSIONS: Results support our hypothesis that intraindividual variability is a more sensitive indicator of subtle cognitive disturbance than conventional speed or accuracy measures and may have potential in the assessment of mild cognitive impairment in patients with non-central nervous system cancers. (JINS, 2018, 24, 724-734).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".