EPI-04DOES DIAGNOSTIC DELAY AFFECT MORBIDITY IN CHILDREN DIAGNOSED WITH BRAIN TUMOURS?
Bibliographic record
Abstract
Cancer diagnosis is not easily concluded due to its low index of suspicion, rarity of the disease, and non-specific presenting features of malignancy. Among all pediatric solid tumours, the greatest diagnostic delay (DD) has been reported in brain tumours. It logically follows that a longer pre-symptomatic interval (PSI) would be associated with survival outcome, more advanced disease stage, or a decreased quality of life. In this study, we use the McMaster Pediatric Brain Tumour Study Group database to report quantitative data and qualitative reports. Using the patients that lied above the 2SD measure, we statistically determined the DD value to be 6 months (time period since first symptoms). In a patient population of 164 from to, aged 0-18 years, we report a 13% rate in DD; interestingly, 85% of those with DD were females. We found a significant relation of PSI with age; older children have a greater delay in diagnosis than younger children. Those with DD had a significantly smaller lesion than those without. Among common symptoms, DD was significantly associated with vomiting and almost half of the DD patients were low-grade astrocytoma patients. Our case reports of patients with DD indicate increased complications, increased morbidity, and decreased quality-of-life during their course of stay in the hospital. Better education of primary care physicians is necessary to create awareness of early signs/symptoms of brain tumours, similar to the HeadSmart initiative in the UK.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".