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Record W4414960061 · doi:10.46989/001c.144582

Treatment Outcomes of Multiple Myeloma in Developing Countries: A Systematic Review and Meta-Analysis

2025· review· en· W4414960061 on OpenAlexaffabout
Jehad Almasri, Bashar Hasan, Zin Tarakji, Faris Naffa, Lynn Warner, Hira Mian, Rajshekhar Chakraborty, Ghulam Rehman Mohyuddin, Zaid Abdel Rahman, Samer Al Hadidi

Bibliographic record

VenueClinical Hematology International · 2025
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMultiple myelomaPomalidomideOverall survivalTransplantationClinical trialAutologous stem-cell transplantationDeveloping countryIxazomibLenalidomide

Abstract

fetched live from OpenAlex

Multiple myeloma (MM) treatment outcomes in developing countries may be impacted by resource constraints. This systematic review and meta-analysis evaluated efficacy outcomes of MM treatments across developing regions. Comprehensive searches in five major databases identified 37 eligible studies from Asia, Africa, Latin America, and Eastern Europe. The Newcastle-Ottawa Scale was used to assess risk of bias. Newer novel agents including daratumumab, carfilzomib, and pomalidomide showed limited use across studies. For patients receiving autologous stem cell transplantation (ASCT), the pooled overall survival rate at longest follow-up (2.5-12.5 years) was 62% (95% CI: 48-75%), with high heterogeneity (I²=92%), while the progression-free survival rate at longest follow-up (3-8 years) was 44% (95% CI: 23-67%). Comparative analyses demonstrated ASCT was associated with significantly superior 5-year survival compared to conventional chemotherapy (RR: 1.59; 95% CI: 1.38-1.82). Bortezomib-based regimens showed better outcomes than thalidomide-based therapies (HR for overall survival (OS) at 4 years: 0.73; 95% CI: 0.53-1.0) and alkylating agent-based regimens (HR: 0.48; 95% CI: 0.28-0.83). Despite resource limitations, ASCT and certain novel agents are associated with improved survival outcomes for MM patients in developing countries. However, substantial heterogeneity in outcomes suggests variability in healthcare infrastructure, treatment accessibility, and clinical expertise across these regions.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0160.003
Bibliometrics0.0010.000
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 head0.224
GPT teacher head0.520
Teacher spread0.295 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

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