Early indicators of primary brain tumours: a population‐based study with 10 years’ follow‐up
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To improve diagnoses of primary brain tumours, knowledge about early indicators is needed. Nationwide Danish health registries were used to conduct a population-based case-control study including all persons diagnosed with a primary brain tumour between 2005 and 2014 in Denmark. METHODS: All 5135 adults diagnosed with a primary brain tumour in the Danish Cancer Registry were matched to 19 572 general population comparisons from the Danish Civil Registration System. Conditional logistic regression analyses were applied to estimate age- and multivariable-adjusted odds ratios (ORs) for the occurrence of a primary brain tumour up to 10 years after hospital diagnoses or prescription of medications related to nervous system diseases and mental and behavioural disorders. RESULTS: Increased odds for primary brain tumour after nervous system diseases and mental and behavioural disorders manifested up to 10 years before tumour diagnosis were found. Increased odds were seen especially for hospital contacts for inflammatory nervous system diseases [OR 11.3; 95% confidence interval (CI) 6.5-19.7], epilepsy (OR 9.0; 95% CI 7.6-10.7) and antiepileptic medications (OR 3.6; 95% CI 3.2-4.0), whilst antidementia medications provided a strong, protective association for primary brain tumours (OR 0.5; 95% CI 0.3-0.8). CONCLUSIONS: Sub-groups of patients diagnosed with or being prescribed certain medications targeting nervous system diseases and mental and behavioural disorders may be at increased risk of being diagnosed with a primary brain tumour. Further studies should disentangle the potential underlying common pathogenetic pathways. The results are important for the development of systematic clinical approaches to ensure early diagnosis of primary brain tumours.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".