Anaplastic Large Cell Lymphoma in Central America: A Report From the Central American Association of Pediatric Hematology Oncology (AHOPCA)
Bibliographic record
Abstract
BACKGROUND: Although anaplastic large cell lymphoma (ALCL) is curable in high-income countries (HIC), data from low- and middle-income countries (LMIC) are lacking. We therefore conducted a retrospective study of the Central American Association of Pediatric Hematology Oncology (AHOPCA) experience in treating ALCL. PROCEDURE: We included all patients age <18 years newly diagnosed with ALCL treated between 2000 and 2013 in seven AHOPCA institutions. Retrospective data were extracted from the Pediatric Oncology Network Database. RESULTS: Thirty-one patients met inclusion criteria. Twenty-five (81%) had advanced disease (stages III and IV), six (19%) were treated on the APO (doxorubicin, prednisone, vincristine) regimen, 15 (49%) on multi-agent chemotherapy designed for T-cell lineage malignancies (GuatALCL protocol), and 10 (32%) on BFM-based treatment regimens. Five-year overall event-free survival and overall survival were, respectively, 67.1 ± 8.6% and 66.7 ± 8.7%. All 10 events occurred in patients treated on BFM-based treatment regimens or the GuatALCL protocol, none on APO treatment: two patients experienced relapse, six treatment related mortality (TRM), and two abandonment. CONCLUSIONS: Treatment of ALCL in countries with limited resources is feasible with similar outcomes as in HIC, though the causes of treatment failure differ. Less intensive regimens may be preferable in order to decrease TRM and improve outcomes. Prospective clinical trials determining the ideal treatment for LMIC children with ALCL are necessary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".