Bibliographic record
Abstract
Recent studies have suggested that adolescent cancer may have better survival outcomes when treated at pediatric centres where they have greater emphasis on enrollment in clinical trials and more capacity to support the social and emotional needs of adolescents. This study investigated 616 cancer cases of those aged 15-18 from 1995 to 2010 in British Columbia, Canada with the data from the Childhood, Adolescent, and Young Adult Cancer Survivors Research (CAYACS) Program by the BC Cancer Research Agency (BCCA). Between 1995 and 2010, only 27% of adolescents in BC were referred to the BC Children’s Hospital (BCCH), the only pediatric oncology centre in BC. This study examined whether referrals to BCCH or BCCA were influenced by age, socioeconomic status, rurality, seasonality, radiotherapy treatment, and different diagnosis based on the International Classification of Childhood Cancer (ICCC) classification. Odds ratios of each modifier to BCCA or BCCH were calculated using univariate and multivariate logistics regression models in R Studio 3.5.1. Overall, 80% of younger adolescent (age 15-16) were referred to BCCH and only 14% of older adolescents (age 17-18) were referred to BCCH, which suggested that older adolescents were less likely than younger adolescents to be referred to BCCH (P value <2e-16). Additionally, leukemia and Central Nervous System cancer (CNS) patients were more likely than all other patients to be referred to BCCH (P value 0.0014). Conversely, patients with germ cell and thyroid carcinomas were less likely than all other patients to be referred to BCCH (P value 0.0029; P value 0.0348). Moreover, driving time to the closest centre for each patient was calculated on ESRI ArcGIS 10.6.1 based on the six-digits postal codes of each patient provided by CAYACS. As driving time increases, patients were less likely to be referred to their closest cancer treatment center.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".