Comparison of Topiramate Concentrations in Plasma and Serum by Fluorescence Polarization Immunoassay
Bibliographic record
Abstract
Topiramate has been recently licensed as an antiepileptic drug. A fluorescence polarization immunoassay (FPIA), the Innofluor, has been developed to determine topiramate in heparinized plasma. Since therapeutic drug monitoring laboratories may not have control over collection of the samples submitted to them, it is important for analytical methods to be robust and able to cope with any specimen. The effect of different anticoagulants on the topiramate FPIA assay was investigated by collecting blood from 50 patients with epilepsy being maintained on a range of topiramate doses as part of their therapy. After venesection the blood was divided among four tubes: plain, heparinized, EDTA, and fluoride/oxalate. Erythrocytes were separated by centrifugation and supernatant fluid frozen to await duplicate assay by FPIA. Results were compared by means of Altman and Bland difference plots which indicated that there was no significant difference between values obtained with heparinized plasma and the other fluids. It was concluded that the Innofluor assay is robust and gives similar results when blood samples are collected into any of the specified anticoagulants.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".