Is anxiety associated with hot flashes in women with breast cancer?
Bibliographic record
Abstract
OBJECTIVE: Women with breast cancer are at higher risk for experiencing hot flashes (HFs), which is attributable, in large part, to systemic cancer treatments and their effects on estrogen levels. However, other factors, such as anxiety, could also play a role. This study aimed to assess the cross-sectional and temporal relationships between anxiety and HFs among women treated for breast cancer and to clarify the direction of these relationships. METHODS: Fifty-six women recently treated for breast cancer were assessed prospectively using a 14-day Hot Flashes and Anxiety Diary (HFAD). Anxiety and HFs were also assessed using the Hospital Anxiety and Depression Scale-anxiety subscale and the Menopause-Specific Quality of Life Questionnaire-vasomotor subscale. In addition, HFs were objectively recorded for a continuous 24-hour period using home-based sternal skin conductance. RESULTS: No cross-sectional relationship was found between anxiety and subjectively assessed HFs, or between anxiety and the frequency and intensity of objectively assessed HFs. However, a greater anxiety level on the HFAD was significantly associated with a shorter time to reach the HF peak, as assessed with sternal skin conductance (partial Spearman correlation coefficient rsp = -0.44). Moreover, greater anxiety predicted more severe self-reported HFs on the following night, both assessed with the HFAD (rsp = 0.13). Conversely, self-reported diurnal and nocturnal HFs on the HFAD did not predict next-day anxiety level. CONCLUSIONS: This study reveals a significant relationship between anxiety and faster-developing objectively measured HFs. Furthermore, anxiety has been found to significantly predict subsequent increases in self-reported HFs, suggesting that strategies that target anxiety could potentially have a beneficial effect on HFs in women with breast cancer.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".