Anxiety in breast cancer patients treated with neoadjuvant therapy: Effects on surgical plan and role of supportive care.
Bibliographic record
Abstract
35 Background: The impact of high anxiety on surgical decision making has been demonstrated in various cancer settings. For patients undergoing neoadjuvant therapy (NAT) for breast cancer, supportive services can be offered prior to surgery and may help them choose between the options of bilateral mastectomy, unilateral mastectomy, or breast conserving surgery (BCS) where clinically appropriate. However, the effect of anxiety at initial diagnosis and psychological support on these decisions has not yet been studied. Methods: A prospective database of breast cancer patients treated with NAT at the British Columbia Cancer Agency was utilized to extract demographic information, surgical plan with regards to BCS and unilateral or bilateral mastectomy, and information about supportive services utilized. This was correlated with anxiety scores at initial consultation recorded by the Edmonton Symptom Assessment System and the Psychosocial Screen for Cancer. Patients were excluded if they had bilateral breast cancer, BRCA mutation, or missing data. Fisher’s exact tests were applied for statistical analysis. Results: From 2012-2016, 361 potential patients were identified. In total, 203 patients met eligibility criteria: 93 patients (46%) had low anxiety and 110 patients (54%) had high anxiety. Patients with high self-reported anxiety at initial consultation were 19% more likely to undergo aggressive surgery (bilateral mastectomy for unilateral disease or mastectomy for BCS eligible disease) than those with low self-reported anxiety at initial consultation (37% VS 18%; p = 0.003). Of the 110 patients with high anxiety, only 46 patients (42%) utilized counselling before surgery. No significant difference in rate of aggressive surgery was observed in patients with high anxiety who had counselling compared to those who did not (33% VS 41%; p = 0.43). Conclusions: High anxiety at initial consultation is associated with a 19% increase in aggressive surgery compared to patients with low anxiety. Counselling resources are currently underutilized by eligible patients, but this did not have an impact on surgical decision making in this study. This may be an area of opportunity for further research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".