The potential for lost productivity in lung cancer patients
Bibliographic record
Abstract
6093 Background: Lung cancer significantly impacts on a patient’s personal and professional life. Little is known about lung cancer patients’ lost productivity, which in turn has an important effect on society. We undertook this descriptive study to learn about lost productivity experienced by lung cancer patients and their caregivers. Methods: 40 consecutive patients attending outpatient lung clinics at a major cancer centre completed questionnaires assessing demographic details, patient and caregiver productivity, and quality of life (EQ5D, FACT-L). Results: 52.5% of respondents were male. Median age was 67 years (range 36 -81). Median disclosed income was $20,000–$39,999; 46.2% had pursued post-secondary training. 70% were ex-smokers, 40% had NSCLC, 27% SCLC, while a third did not know their diagnosis. Treatments received included IV chemotherapy (62.5%), oral therapy (20%), radiation (55%), surgery (42.5%) and 5% no treatment. Over 25% were working full-time prior to diagnosis, 40% were retired. None were able to continue full-time employment; 20% required disability or sick leave. 8 (20%) were able to work part-time. Of those still working, a median of 14 h were missed due to illness in the preceding 3 weeks, with a median of 32 h worked in that period. Patients reported an overall moderate (5/10) impact on their productivity and a significant (6/10) impact on their daily activities attributable to their cancer. Only 8.5% of patients received paid assistance, while 76% had their spouse, relative or friend as an unpaid caregiver. In the preceding 3 weeks, caregivers who assisted patients provided a median 24h of care; 25% of caregivers missed a median 12h of work. Overall, mobility, self-care and anxiety/depression were rated as mildly affected (1/3), while daily activities and pain/discomfort were rated as moderately affected (2/3). Median overall health state rated by the respondents was 60 {scale 0(worst)-100(best)}. Quality of life overall was poor - FACT-L median score was 93.2 (range 50 to 125). Conclusion: Lung cancer negatively impacts work productivity and significantly impairs activity. While many lung cancer patients are retired, there is a significant burden on caregivers, which may result in a substantial burden to society in lost productivity. 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".