MétaCan
Menu
Back to cohort
Record W4214497236 · doi:10.14740/jh971

Consensus Statement: Importance of Timely Access to Multiple Myeloma Diagnosis and Treatment in Central America and the Caribbean

2022· article· en· W4214497236 on OpenAlexvenueno aff
Mayra Pimentel, Ondina Espinal, Darwin Martínez, Ninotchka Mendoza, Anarellys Quintana, Juan Enrique Richmond

Bibliographic record

VenueJournal of Hematology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultiple myelomaConsensus conferenceDiseaseCaribbean regionIntensive care medicineFamily medicinePolitical sciencePathologyLatin AmericansImmunology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.338
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of HematologySame topicMultiple Myeloma Research and TreatmentsFrench-language works237,207