A comparative study of referral patterns and management of patients with malignant brain tumours in Birmingham, UK, and Toronto, Canada
Bibliographic record
Abstract
The management of malignant brain tumours comprises a huge financial burden to any health care system. The referral pattern and initial management of this condition has not been widely investigated. Our main aim was to investigate and compare the referral patterns between two tertiary referral centres in Birmingham, UK, and Toronto, Canada, which have similar health care provision structures, but different financial resources, in order to examine whether there are any significant delays in the pathway of either system. The investigation consisted of a retrospective analysis of case records of patients identified as having malignant brain tumours who had been operated on at the two centres during the period 1997-1998. Data were collected on patient demographics, symptomatology at presentation, referral pattern, and time intervals between presentation, investigations, neurosurgical consultation and operation. Mann-Whitney U-tests of statistical significance were used to compare the two centres. Sixty-eight and 100 patients in Birmingham and Toronto, respectively, were identified from the registers following exclusion of patients with benign or low-grade tumours. Both centres were found to be similar with respect to sociodemographic factors of patients with malignant brain tumours. Referral patterns were analysed for presenting symptoms of neurological deficit and seizure, and found to be similar for both centres. Time intervals from presentation to investigation and from investigation to neurosurgical consultation were not significantly different between the two centres. Significant differences were found from neurosurgical consultation to operation (Birmingham, 3 days, and Toronto, 6 days, p = 0.006), and the duration of stay in hospital (Birmingham, 11 days, and Toronto, 2 days, p < 0.001). Although there are differences in the financial resources of the two tertiary centres our study reveals no significant differences in the referral pathway to operation.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".