A population based perspective on children and youth with brain tumours
Bibliographic record
Abstract
Abstract Background There is currently no active surveillance of metastatic and non-malignant brain tumours in Canada as well as data on the health service use of children and youth with brain tumours. The objective of this study was to identify pediatric primary, metastatic, benign, and unspecified brain tumours in Ontario, Canada and to describe their health service use from a population based perspective. Methods The population based healthcare administrative databases National Ambulatory Care Reporting System and the Discharge Abstract Database were used. Patients with malignant (primary and metastatic), benign, and unspecified brain tumours in acute care between fiscal year 2003/04 and 2009/10 were identified using specified International Classification of Diseases version ten codes. Results Between fiscal year 2003/04 and 2009/10, there were 4022 brain tumour episodes of care (18.4 per 100,000 children and youth). Malignant brain tumors had the highest rates of episodes of care (14.9 times higher than that of benign and 5.7 times higher than that of unspecified brain tumours). Compared to patients with malignant brain tumours, those with benign brain tumours spent a longer period of time in acute care (p
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".