Bibliographic record
Abstract
e17540 Background: Globally, over 900,000 women have been diagnosed with ovarian cancer in the last five years with approximately 7 of every 10 women diagnosed in advanced stages. Projections are that ovarian cancer will claim more than 8 million lives between 2022-2050 without better control measures. Methods: Our prevalence-based cost-of-illness approach uses a societal perspective to estimate the burden of ovarian cancer in 11 countries. We quantify the annual socioeconomic costs to patients, caregivers, health systems, and the economy. We include both direct costs - medical costs across the care continuum - and non-medical costs - including patient time travelling to and receiving care. Indirect costs included: 1) labour force productivity losses due to cancer-attributable absenteeism, presenteeism, and labour force dropout; and 2) informal caregiving costs. We also report the societal value of lives lost due to ovarian cancer. We developed an Excel-based tool using data from the the World Ovarian Cancer Coalition's Every Woman Study and the WHO’s Cancer Programme, to develop a micro-costing framework that assesses resources and costs of providing care for ovarian cancer, and data from novel SLRs and meta-analyses that assessed the effect of cancer on patient labour productivity outcomes and the time caregivers spent caring for people living with cancer. Results: In 11 countries, we estimated USD 70 billion in socioeconomic losses due to ovarian cancer. Patients spent 3,663 years traveling to—or receiving treatment. Women lost labour productivity equivalent to 2.5 million workdays due to ill-health from ovarian cancer and 9,403 women living with ovarian cancer or survivors were missing from the workforce. Caregivers spent 17,112 person years providing practical support to patients, —an average of 33 days per woman living with ovarian cancer. Conclusions: We find substantial socioeconomic costs accruing to a range of stakeholders. It is imperative to align strategies that can prevent ovarian cancer, while also strengthening support for patients, caregivers, and health systems. Socioeconomic costs attributable to ovarian cancer, by category & country (2022 USD, millions). Cost totals Australia Canada UK USA Colombia Kazakhstan Malaysia India Kenya Nigeria Malawi ALL HE (% Total Health Expenditure) 187.9 (0.10%) 391.7 (0.16%) 653.7 (0.19%) 2,378.0 (0.06%) 100.0 (0.35%) 45.6 (0.59%) 49.6 (0.30%) 1313.4 (0.13%) 8.3 (0.16%) 85.5 (0.48%) 0.8 (0.08%) 5,214.4 Patient time 16.9 21.2 32.5 181.2 1.9 1.2 2.4 22.0 0.3 0.6 0.1 280.3 Productivity 29.5 38.9 52.5 312.5 2.8 3.3 7.6 15.3 0.8 1.6 0.3 465.1 Informal caregiving 33.0 69.3 95.7 228.0 4.4 1.2 6.3 28.8 1.8 2.5 0.8 228.0 Mortality costs 2,383.4 4,311.4 6,724.0 48,740.0 150.0 124.6 326.9 776.0 18.1 42.5 0.8 63,597.5 SEB (% of GDP) 2,650.7 (0.16%) 4,832.4 (0.22%) 7,558.3 (0.24%) 51,839.5 (0.20%) 259.1 (0.08%) 175.8 (0.08%) 392.8 (0.10%) 2,155.5 (0
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".