Psychosocial well-being assessment in women with breast cancer.
Bibliographic record
Abstract
207 Background: A breast cancer (BC) diagnosis can affect psychosocial wellbeing. The goal of the current study is to identify the severity of and specific risk factors for depression, anxiety, and quality of life impairment in a sample of BC patients. Early identification of at risk individuals can expedite appropriate referrals and interventions. Methods: Data from 53 female BC patients referred to a Behavioral Medicine service at a large academic medical center in 2013-2015 by medical providers who identified distress at routine clinic visits were analyzed. Patients completed the Center for Epidemiology Studies Depression Scale, State Trait Anxiety Inventory, McGill Pain Questionnaire, and the Short Form 12 Quality of Life Inventory as part of their initial assessment following referral. Demographic factors, disease and treatment related factors were analyzed for correlation with psychosocial wellbeing. Results: Mean age was 52. The majority of the population were Caucasian (79%), and 53% were married. All stages of disease were represented. Significant depression, anxiety and psychological quality of life impairment were seen in 53%, 50%, and 39% of patients respectively. There was a trend towards a significant difference in higher anxiety scores in patients who were not on chemotherapy (M=44.2, SD 5.4) compared to those on chemotherapy at the time of assessment (M=38.54, SD 9.3; t (51) = -1.94 p=0.077). A positive correlation was seen between depression and pain scores (r 0.294, p=0.038). Depression and psychological quality of life scores were negatively correlated (r -0.632 p<0.001), as were pain and physical quality of life scores (r -.343, p 0.024). There was no correlation between higher rates of depression or anxiety with type of surgical intervention or stage of disease. Conclusions: Significant depression, anxiety and quality of life impairment were seen in a large percentage of BC patients referred to Behavioral Medicine for perceived distress, regardless of type of surgery and disease stage. Higher rates of anxiety were seen in patients referred while not on chemotherapy compared to patients who were on chemotherapy at the time of referral. Early referral is the key to proper identification and treatment of high risk individuals.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".