Cognitive function, fatigue, and menopausal symptoms in breast cancer patients receiving adjuvant chemotherapy: evaluation with patient interview after formal assessment
Bibliographic record
Abstract
BACKGROUND: Women who receive adjuvant chemotherapy for breast cancer report fatigue, menopausal symptoms and cognitive problems. Here we compare assessment of these symptoms using self-report questionnaires and a researcher-administered screen of cognitive function with the experience of women as revealed in a semi-structured interview. METHODS: Twenty-one women who were receiving adjuvant chemotherapy completed the Functional Assessment of Cancer Treatment-General (FACT-G) self-report questionnaire, and sub-scales for fatigue (FACT-F) and endocrine symptoms (FACT-ES). They were evaluated for cognitive dysfunction using the High Sensitivity Cognitive Screen (HSCS). They then completed a semi-structured interview, which explored the nature and severity of these symptoms and their impact on daily function. RESULTS: All patients experienced fatigue and most had menopausal symptoms. There was reasonable correlation of findings in the interview with FACT-F and FACT-ES scores. The HSCS revealed fewer problems than were reported by patients, and correlated with patient experience only for the domain of memory. Most patients noted adverse changes in other cognitive domains, especially concentration, with substantial effects on every-day function. CONCLUSIONS: Women receiving adjuvant chemotherapy for breast cancer have substantial problems with fatigue, menopausal symptoms and cognitive changes. Formal tests such as the HSCS may fail to adequately capture the perceived impact of symptoms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".