Table_1_Investigation of incidence and geographic distribution of gliomas in Canada from 1992 to 2010: a national population-based study highlighting the importance of exposure to airport operations.docx
Bibliographic record
Abstract
Background<p>Gliomas account for over two-thirds of all malignant brain tumors and have few established risk factors beyond family history and exposure to ionizing radiation. Importantly, recent studies highlighted the exposure to ultrafine particles (UFP) as a putative risk factor for malignant brain tumors.</p>Methods<p>Clinical and geographic data encompassing all provinces and territories from 1992 to 2010 was obtained from the Canadian Cancer Registry and Le Registre Québécois du Cancer. Linear regression and joinpoint analyses were performed to assess incidence trends. Significantly higher and lower incidence postal codes were then interrogated using Standard Industrial Classification codes to detect significant industrial activity.</p>Results<p>In Canada, between 1992 and 2010, there were ~32,360 cases of glioma. Of these, 17,115 (52.9%) were glioblastoma. The overall crude incidence rates of 5.45 and 2.87 cases per 100,000 individuals per year for gliomas and glioblastomas, respectively, were identified. Our findings further revealed increasing crude incidence of gliomas/glioblastomas over time. A male predominance was observed. Provinces leading in glioma incidence included Quebec, Nova Scotia, and New Brunswick. Significantly lower crude incidence of glioma was found in Nunavut, Northwest Territories, Ontario, and Alberta. A putative regional clustering of gliomas was observed, with higher incidence rates in postal code areas correlating with industrial activity related to airport operations.</p>Conclusion<p>This study describes the geographic distribution of the glioma disease burden and, potentially, identifies industrial activity related to airport operations as potentially being associated with higher incidence of this cancer.</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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".