Objective–subjective disparity in cancer‐related cognitive impairment: does the use of change measures help reconcile the difference?
Bibliographic record
Abstract
OBJECTIVE: Studies to date have found little correlation between subjective and objective measures of cognitive function in cancer patients, making it difficult to interpret the significance of their cognitive complaints. The purpose of this study was to determine if a stronger correlation would be obtained using measures of cognitive change rather than static scores. METHODS: Sixty women with early stage breast cancer underwent repeated cognitive assessment over the course of chemotherapy with a neuropsychological test battery (objective measure) and with the FACT-Cog (subjective measure). Their results were compared to 60 healthy women matched on age and education and assessed at similar intervals. We used multilevel modeling, with FACT-Cog as the dependent measure and ordinary least squares slopes of a neuropsychological summary score as the independent variable, to evaluate the co-variation between the subjective and objective measures over time RESULTS: Measures of both objective and subjective cognitive function declined over the course of chemotherapy in the breast cancer patients but there was no significant relationship between them, even when using change measures. Change in objective cognitive function was not related to change in anxiety or fatigue scores but the decline in perceived cognitive function was associated with greater anxiety and fatigue. CONCLUSIONS: The discrepancy in objective and subjective measures of cognition in breast cancer patients cannot be accounted for in terms of a failure to use change measures. Although the results are negative, we contend that this is the more appropriate methodology for analyzing cancer-related changes in cognition.
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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 | 0.013 | 0.051 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".