The burden of skeletal-related events in four Latin American countries: Argentina, Brazil, Colombia, and Mexico
Bibliographic record
Abstract
AIM: Skeletal-related events (SREs) are major bone complications that frequently occur in patients with solid tumors (ST) and bone metastases, and in patients with multiple myeloma (MM). SREs include pathological fracture, spinal cord compression, radiation to bone, and surgery to bone. Limited data are available regarding the burden of SREs in Latin America. We built an economic model to quantify the current and future economic burden of SREs among adults in four Latin American countries: Argentina, Brazil, Colombia, and Mexico. METHODS: A comprehensive literature review with a systematic search strategy was conducted to parameterize the economic burden of illness (BOI) model. Economic analyses were conducted using a prevalence-based model. Aggregate SRE costs obtained from country-specific sources were used. We also included patient productivity losses. Costs were expressed in 2020 USD for the total annual burden, annual burden per 1,000 at risk, and projected five-year burden. RESULTS: The estimated total number of SREs was 251,503 in 2020, amounting to a total annual cost of USD 1.4 billion. The total projected five-year cost was USD 6.9 billion. Annual costs were highest in Brazil (USD 779.1 million), followed by Mexico (USD 281.8 million), Argentina (USD 174.6 million), and Colombia (USD 120.1 million). The average financial burden per 1,000 at risk was greatest in Brazil (USD 3.6 million), followed by Mexico (USD 3.4 million), Colombia (USD 2.9 million), and Argentina (USD 2.7 million). CONCLUSION: Despite recommendations by medical societies for the use of bone-targeted agents in patients with solid tumors and bone metastasis or with multiple myeloma and bone lesions, a large proportion of patients at risk of experiencing SREs are not treated. Early detection of bone metastases and SREs and the use of the most effective preventative treatments are needed to decrease the clinical and economic burden of SREs in Latin America.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".