Bibliographic record
Abstract
CONTEXT: Refusal of treatment for childhood cancer engenders much discussion. No systematic study of this phenomenon exists in countries where access to treatment is readily available. OBJECTIVE: To identify and describe all published cases of treatment refusal for childhood cancer in the contemporary era. DATA SOURCES: We searched PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, LexisNexis Academic, personal database, and secondary bibliographies. STUDY SELECTION: Eligible studies included at least 1 child <18 years of age and addressed refusal of medically recommended interventions intended to cure cancer. DATA EXTRACTION: Cases were analyzed with respect to key features, including demographics, rationale for refusal, legal action, and medical outcome; data were combined for multiple publications discussing the same case. RESULTS: Of 4342 unique publications identified, 579 were eligible after screening; 96 scholarly articles and 19 judicial opinions addressed 73 unique cases of treatment refusal. Most cases occurred in the United States. Rationales for refusal were broadly grouped into 4 categories. Fifty-one cases (70%) involved legal action at the time of refusal. Legal action did not reliably predict survival. : Publication bias and missing data, especially for cases without legal action, were limitations. CONCLUSIONS: We identified important gaps in the literature, including the significant variation in approaches and lack of consensus regarding the prognostic threshold necessary for compelling treatment and the absence of voices of children and adolescents who have received treatment over their families' objections. More research reporting effective strategies for working with families who refuse is needed.
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 | 0.020 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".