Consensus Statement: Importance of Timely Access to Multiple Myeloma Diagnosis and Treatment in Central America and the Caribbean
Bibliographic record
Abstract
Background: In Central America and the Caribbean, multiple myeloma (MM) patients face significant barriers to diagnosis and treatment. The aim of this study is to describe the current situation of MM in the region, discuss the current barriers to timely diagnosis and proper treatment, and develop consensus recommendations to address these issues. Methods: Nine experts from five countries took part in a virtual consensus meeting on MM in Central America and the Caribbean. During the meeting, experts analyzed the disease burden, the current conditions for disease management, and access to treatment in the region. The participants reached a consensus on the extent of the problem and the necessary measures. Results: Hard evidence on the incidence and prevalence of MM in the region is scarce, but the experts perceive an increase in MM cases. The lack of data on the direct and indirect costs at the local and regional levels obscures the impact of the disease and limits awareness among decision-makers. Most patients are diagnosed late and face long waiting times and geographical barriers to access treatment. Access to efficacious innovative therapies that increase survival time is limited due to access barriers within health systems. Conclusions: There was consensus on five recommendations: 1) to generate evidence; 2) to educate the public; 3) to increase timely diagnosis and facilitate access to treatment; 4) to promote interaction, collaboration, and participation among all sectors involved in the decision-making process; and 5) to guarantee timely access to new therapies.
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.
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 | 0.103 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.025 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".