Seasonal fluctuations in psychological distress amongst women diagnosed with early breast cancer receiving radiotherapy
Bibliographic record
Abstract
OBJECTIVE: Seasonal effects on patients diagnosed with depression/anxiety-related psychological disorders have varying impacts on symptom severity. Seasonal changes in psychological distress may be due to decreased daylight exposure during the fall/winter seasons. Patients receiving radiation therapy (RT) for early-stage invasive breast cancer (EIBC) are at high risk for developing depressive symptoms. Of interest is whether seasonal factors influence the psychological symptoms of patients being treated for EIBC. METHODS: Patients treated with RT for EIBC between January 2011 and June 2017 were identified. Patients who completed at least one Edmonton Symptom Assessment Scale (ESAS-r) pre-RT and post-RT were included in our analysis. Patients receiving RT during the autumn and winter (November-March) were compared with patients receiving RT during the spring and summer (April-August). Psychological distress was evaluated based on patient-reported depression, anxiety, and overall wellbeing on the ESAS-r. Data on systemic treatment and radiation were extracted from existing databases. RESULTS: Eight-four patients treated with RT in spring/summer and 102 patients treated with RT in autumn/winter were included. Patients receiving RT during spring/summer had better wellness score prior to RT, compared with those receiving RT during winter/autumn (P = .03). However, patients receiving RT in the spring/summer had worse symptom trajectories across three domains of depression, anxiety, and wellbeing (P = .03, P = .008, and P < .0001, respectively). CONCLUSIONS: Seasonality influenced the symptoms reported by patients with EIBC receiving RT. Future studies are needed to understand when during treatment patients are at highest risk for psychological distress and how seasonality may influence high-risk periods.
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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 | 0.002 |
| 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".