Prevalence and predictors of abandonment of therapy among children with cancer in El Salvador
Bibliographic record
Abstract
Abandonment of therapy is one of the most common causes of treatment failure among children with cancer in low-income countries. Our objectives were to describe the prevalence and predictors of abandonment among such children with cancer in El Salvador. We analyzed data on patients younger than 16 years, diagnosed with any malignancy between January 2001 and December 2003 at the Benjamin Bloom National Children's Hospital, San Salvador. Among 612 patients, 353 were male (58%); the median age at diagnosis was 5.1 years; 59% of patients were diagnosed with leukemia/lymphoma, 28% with solid tumors and 13% with brain tumors. The prevalence of abandonment was 13%. Median time to abandonment was 2.0 (range 0-36) months. In univariate analyses, paternal illiteracy [odds ratio (OR) 3.8, 95% confidence interval (CI) 2.0-7.2; p = 0.001]; maternal illiteracy (OR = 5.1, 95% CI 2.5-10; p < 0.0001); increasing number of household members (OR = 1.2, 95% CI 1.1-1.3; p = 0.004); and low monthly household income (OR per $100 = 0.59, 95% CI 0.45-0.75; p < 0.0001) all significantly increased the risk of abandonment, whereas travel time to hospital did not. In multiple regression analyses, low monthly income and increased number of people in the household were independently predictive of abandonment. In conclusion, in El Salvador, despite the provision of free treatment, socioeconomic variables significantly predict increased risk of abandonment of therapy. Understanding the pathways through which socioeconomic status affects abandonment may allow the design of effective interventions.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".