P.045 Use of sodium bicarbonate to alkalinize the urine in pediatric patients treated with Topiramate (pilot study)
Bibliographic record
Abstract
Background: Topiramate is an antiepileptic frequently used in pediatrics with multiple mechanisms of action. This includes carbonic anhydrase inhibition, which has unclear relevance to its antiepileptic effect. Metabolic acidosis, hypocitraturia and nephrolithiasis are known side-effects of carbonic anhydrase inhibition and can limit therapeutic effect. Alkali therapy may normalize acidosis, increase urinary citrate, and reduce nephrolithiasis risk. We hypothesize that provision of sodium bicarbonate supplementation to patients with topiramate-induced acidosis will mitigate these side-effects without worsening seizure frequency or severity. Methods: Pediatric patients on antiepileptic therapy with topiramate are being recruited from outpatient pediatric neurology clinics at McMaster Children’s Hospital. We aim to recruit 20 patients with metabolic acidosis and 20 control patients. Measures include blood gas, electrolytes, urine electrolytes and citrate. Patients with metabolic acidosis will be given daily sodium bicarbonate for one month, followed by repeat bloodwork. Seizure frequency will be prospectively documented in all participants throughout the three-month period. Results: Recruitment is ongoing, and three patients (1 with acidosis) have been recruited thus far. Results will be analyzed with chi-squared and paired T tests. Conclusions: This pilot study is the first to evaluate the safety and efficacy of sodium bicarbonate supplementation in patients receiving topiramate for seizure control.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".