Treatment abandonment and refusal among children with central nervous system tumors in Jordan
Bibliographic record
Abstract
BACKGROUND: Treatment abandonment and refusal are reported to contribute significantly to poor survival of children with cancer in low- and middle-income countries. We aimed to assess this phenomenon among children diagnosed with central nervous system (CNS) tumors in Jordan. METHODS: We retrospectively reviewed the medical charts of children <18 years diagnosed with CNS tumors (2010-2020). Patients who abandoned or refused part of treatment were reviewed for their clinical characteristics, social circumstances, and possible reasons. We excluded patients referred for second opinion, radiotherapy only, or who traveled abroad for treatment. RESULTS: Four hundred seventy-three Jordanian children were identified; 12 families (2.5%) abandoned treatment, and 15 refused part of therapy (3%). Most patients were females (67%) and most had good or moderate performance status (89%). Most families (93%) lived within 2 hours from King Hussein Cancer Center. Most parents were university graduates (71%) and all fathers were employed, while 71% of mothers were housewives. The most common reasons to abandon or refuse therapy were treatment intensity in view of poor tumor outcome or bad quality of life, conflicting recommendations from other health care providers, "personal beliefs" against chemotherapy, and preference to use alternative medicine. CONCLUSIONS: Treatment abandonment and refusal in Jordanian children with CNS tumors is low. Universal cancer insurance, high level of education in the country, centralized cancer care in one institution, and the twinning program likely contributed to our low incidence. Improving knowledge on CNS tumors and better community rehabilitation and supportive services may help further decrease the abandonment and treatment refusal rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".