Cancer-related cognitive impairment in breast cancer survivors: An examination of conceptual and statistical cognitive domains using principal component analysis
Bibliographic record
Abstract
There is a great deal of variability in the composition of neuropsychological test batteries used in the assessment of cancerrelated cognitive impairment (CRCI). Not only the development of a gold standard approach for CRCI assessment would allow for easier identification of women suffering from CRCI but it would also promote optimal care for survivors. As a first step towards the development of a valid and reliable unified test battery, the objective of this study was to verify whether the theoretical domains commonly used in CRCI assessment are statistically supported, before and after breast cancer treatment. Principal component analyses (PCA) were performed on the results from 23 neuropsychological tests grouped into eight conceptual domains. For baseline data, the Kaiser-Meyer-Olkin was .82 and Bartlett’s χ2(253, N=95) = 949.48, P<0.001. A five-component solution explained 60.94% of the common variance. For the post-treatment data, the Kaiser-Meyer-Olkin was .83 and Bartlett’s χ2(253, N=95) = 1007.21, P<0.001 and a five component solution explained 62.03% of the common variance. Although a visual comparison of the theoretical model with those determined via PCA indicated important overlap between conceptual domains and statistical components, significant dissimilarities were also observed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".