Bibliographic record
Abstract
Timely access to quality healthcare has become an increasingly important public health concern over the years. Early diagnosis of cancer is a fundamental goal in oncology because it allows an opportunity for timely treatment while disease burden is still in its earliest stages. Consequently, prognosis may improve, and a cure can be attained with minimal side or late effects. This review examined delays present in diagnosis of childhood cancers and factors that influence these delays. An extensive search of the literature published before April 15, 2007 was conducted for studies that evaluated any type of delay along the cancer-care continuum. Twenty-three studies were included. Diagnosis delay varied across studies. Physician delays were generally longer than those consequent to parents' or patients' recognition of underlying disease. Causes of delays can be grouped into 3 categories: patient and/or parent, disease, and healthcare. The main factors related to diagnosis delay were the child's age at diagnosis, parent level of education, type of cancer, presentation of symptoms, tumor site, cancer stage, and first medical specialty consulted. Greater understanding of factors that influence delays and the individual impact of patient and provider delays on disease severity and prognosis would be useful to form effective policies and programs aimed at ensuring timely access to healthcare for children with cancer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".