Expansion of the quality of care index on breast cancer and its risk factors using the global burden of disease study 2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC), as the top neoplasm in prevalence and mortality in females, imposes a heavy burden on health systems. Evaluation of quality of care and management of patients with BC and its responsible risk factors was the aim of this study. METHODS: We retrieved epidemiologic data of BC from the Global Burden of Disease (GBD) 1990-2019 database. Epidemiology and burden of BC and its risk factors were explored besides the Quality of Care Index (QCI) introduced before, to assess the provided care for patients with BC in various scales. Provided care for BC risk factors was investigated by their impact on years of life lost and years lived with disability by a novel risk factor quality index (rQCI). We used the socio-demographic index (SDI) to compare results in different socio-economic levels. RESULTS: In 2019, 1,977,212 (95% UI: 1,807,615-2,145,215) new cases of BC in females and 25,143 (22,231-27,786) in males was diagnosed and this major cancer caused 688,562 (635,323-739,571) deaths in females and 12,098 (10,693-13,322) deaths in males, globally. The all-age number of deaths and disability-adjusted life years attributed to BC risk factors in females had an increasing pattern, with a more prominent pattern in metabolic risks. The global estimated age-standardized QCI for BC in females in 2019 was 78.7. The estimated QCI was highest in high SDI regions (95.7). The top countries with the highest calculated QCI in 2019 were Iceland (100), Japan (99.8), and Finland (98.8), and the bottom countries were Mozambique (16.0), Somalia (8.2), and Central African Republic (5.3). The global estimated age-standardized rQCI for females was 82.2 in 2019. CONCLUSION: In spite of the partially restrained burden of BC in recent years, the attributable burden to risk factors has increased remarkably. Countries with higher SDI provided better care regarding both the condition and its responsible risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it