93 Children with Headaches: Clinical Predictors of Significant Neurologic Problems
Bibliographic record
Abstract
To determine clinical predictors contributing to the diagnosis of children with a neurologic catastrophe who present with headache. A 10-year retrospective case-control study of all children, aged 4–18, with no known neurologic disorder, diagnosed with a neurologic catastrophe (brain tumour, cerebral haemorrhage, pseudotumor cerebri, cerebro-vascular malformation, brain abscess and acute hydrocephalus) who presented to our centre with a chief complaint of headache. The characteristics of the identified cases and there clinical manifestations were compared with matched controls complaining of headaches found to have a normal brain imaging. A logistic regression included all the variables found to have a significant association in a univariate analysis (Mann-Whitney U and Chi-square tests). Thirty cases (4 patients with astrocytomas, 6 pseudotumors cere-bri, 2 glioblastomas, 9 cerebral haemorrhage, 2 ependymomas, 3 medulloblas-tomas, 2 brain abscesses, 1 pineal dysgerminoma, 1 choroid plexus carcinoma, 1 optic glioma) and 30 controls were identified and compared. The majority of children suffering from a neurologic catastrophe had an abnormal neurological exam (p<0.0001), had no family history of migraine (p<0.006), presented vomiting (p<0.008) and had the headache for a shorter duration 5 weeks ±90 (SD) neurologic catastrophe VS 71 weeks ±108 (SD) other headache; p<0.001). On history, not having a family history of migraine resulted in an odds ratio of 12 (p<0.05) and having a history of vomiting resulted in an odds ratio of 17.5 (p<0.05). Having an abnormal neurological exam was associated with an odds ratio of 278 (p<0.05). We consider that our clinical predictors could help in guiding the appropriate use of brain imaging when they will be tested in a prospective study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".