Impact of recurrence on employment, finances, and productivity for early-stage cancer patients and caregivers: US survey
Bibliographic record
Abstract
Background Following an early-stage cancer diagnosis, recurrences can occur. To quantify financial impacts of a first recurrence, we surveyed patients and caregivers.Methods The survey was self-administered online to patients (N = 202) with early-stage bladder, gastric, head and neck, melanoma, non–small cell lung, renal cell, and triple-negative breast cancers that recurred and caregivers (N = 100) of such patients. Work productivity and financial impacts were explored.Results Negative impacts on work productivity, employment, finances, and healthcare resource use were identified, with significant differences seen across cancer types, between locoregional and distant/metastatic recurrences, and from pre-recurrence to post-recurrence.Conclusions The financial burden to patients, caregivers, healthcare systems, and society following early-stage cancer recurrence is substantial. Treatments that decrease recurrences can reduce this burden.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".