A needs assessment of pediatric epilepsy surgery in Haiti
Bibliographic record
Abstract
OBJECTIVE: Epilepsy disproportionately affects low- and/or middle-income countries (LMICs). Surgical treatments for epilepsy are potentially curative and cost-effective and may improve quality of life and reduce social stigmas. In the current study, the authors estimate the potential need for a surgical epilepsy program in Haiti by applying contemporary epilepsy surgery referral guidelines to a population of children assessed at the Clinique d'Épilepsie de Port-au-Prince (CLIDEP). METHODS: The authors reviewed 812 pediatric patient records from the CLIDEP, the only pediatric epilepsy referral center in Haiti. Clinical covariates and seizure outcomes were extracted from digitized charts. Electroencephalography (EEG) and neuroimaging reports were further analyzed to determine the prevalence of focal epilepsy or surgically amenable syndromes and to assess the lesional causes of epilepsy in Haiti. Lastly, the toolsforepilepsy instrument was applied to determine the proportion of patients who met the criteria for epilepsy surgery referral. RESULTS: Two-thirds of the patients at CLIDEP (543/812) were determined to have epilepsy based on clinical and diagnostic evaluations. Most of them (82%, 444/543) had been evaluated with interictal EEG, 88% of whom (391/444) had abnormal findings. The most common finding was a unilateral focal abnormality (32%, 125/391). Neuroimaging, a prerequisite for applying the epilepsy surgery referral criteria, had been performed in only 58 patients in the entire CLIDEP cohort, 39 of whom were eventually diagnosed with epilepsy. Two-thirds (26/39) of those patients had abnormal findings on neuroimaging. Most patients (55%, 18/33) assessed with the toolsforepilepsy application met the criteria for epilepsy surgery referral. CONCLUSIONS: The authors' findings suggest that many children with epilepsy in Haiti could benefit from being evaluated at a center with the capacity to perform basic brain imaging and neurosurgical treatments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".