Descriptive study on the lost productivity in breast cancer patients
Bibliographic record
Abstract
6542 Background: There is a paucity of data assessing the potential impact of breast cancer diagnosis on the productivity of affected women. The objective was to identify and quantify lost productivity, health utilities and quality of life experienced in women diagnosed with breast cancer. Methods: A consecutive cross-sectional cohort of women with breast cancer (at any stage) attending outpatient clinics at Mount Sinai Hospital/Princess Margaret Hospital were eligible and consented to participate in the study. Women completed questionnaires assessing demographic and disease related information, work productivity and activity impairment utility (EQ5D VAS) and quality of life (FACT-B). Results: Data from 103 patients age 56.5 ± 11.9 years (mean +SD) were collection. Distribution of stage at diagnosis was as follows: 0 (31%), I (26%), II (10%), III (5%), IV (17%), unknown (11%). Time since diagnosis was 30.0 ± 39.1 months. Most women had recently been on active treatment for their breast cancer: chemotherapy (47%), hormone manipulation (23%), herceptin (6%), radiation (27%) and unknown (15%). 9% of women had metastatic disease, 35% had an income between $0 and $30,000. 58% of women were working full time for pay before their diagnosis, whereas only 19% were working full time for pay at the time of the assessment. At the time of the assessment, 18% were on disability leave. 8.7% of the women retired between the times of their diagnosis to the current assessment. Of those still working, a mean of 8.7 ± 11.6 days were missed from work in the previous 30 days due to problems related to breast cancer. The average number of days that employed patients actually worked (N=27) was 16.0 ± 9.0 days (range 4–30 days). 8% of patients required paid health care assistance during the past 4 weeks. 44% of patients had a spouse as an unpaid caregiver, followed by child/parent (20%) and friend (13%). Mean overall health rated by the respondents using the EQ5D VAS was 73.2 ± 16.3. The FACT-B mean was 68.0 ± 12.5 (range 27 to 98). Conclusion: Breast cancer negatively impacts work productivity and overall activity. The significant use of both paid and unpaid assistance would amount to significant societal costs which are currently not included in most cost-effectiveness analyses. No significant financial relationships to disclose.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".