Intra-individual Variability in Women with Breast Cancer
Bibliographic record
Abstract
Studies assessing cognitive functioning in women treated for breast cancer have used primarily standardized neuropsychological tests and examined accuracy and/or reaction time as outcome measures: they have been inconsistent in identifying the cognitive domains affected and the severity of deficits. In other contexts of neural development and disorders, measures of Intra-individual variability (IIV) have proven useful in identifying subtleties in performance deficits that are not captured by measures of central tendency. This article presents proof of concept that assessing IIV may also increase understanding of the cognitive effects of cancer treatment. We analyzed mean accuracy and reaction time, as well as IIV from 65 women with breast cancer and 28 age and education matched controls who performed the Conner's Continuous Performance Test, a "Go-NoGo" task. Although there were no significant differences between groups using measures of central tendency, there was a group × inter-stimulus interval (ISI) interaction for IIV Dispersion (p < .001). Patient Dispersion was more variable at shorter ISI than controls and less variable at long ISI, suggesting greater sensitivity to presentation speed. Interpretation of IIV differences requires further investigation. Our results suggest that future studies would benefit from designs that allow analysis of IIV measures in studies assessing cognition in cancer survivors.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".