Impact of an Oral Theophylline Loading Dose Pre-Electroconvulsive Therapy
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the safety and impact of an oral theophylline loading dose calculated to achieve a 10- to 15-mg/L plasma concentration when administered 1.5 hours before electroconvulsive therapy (ECT). METHODS: We conducted a retrospective study using inpatient hospital records between January 2007 and June 2012 at the Dr. Georges L. Dumont University Hospital Centre. Patients receiving a series of ECTs with a calculated theophylline loading dose were selected. Variables collected include ECT parameters for each ECT, medications received, and treatment-related side effects. RESULTS: We identified 35 patients and analyzed 14 who had no treatment modifications except for the addition of theophylline. The mean predicted theophylline plasma concentration was 12.99 (SD, 1.09) mg/L with dosages ranging from 260 to 600 mg. Eight patients (89%) with abortive seizures and 4 (80%) with missed seizures achieved a seizure duration of greater than 15 seconds with theophylline. Seizure duration increased by 165.6% (+21.3 seconds; P = 0.048) with theophylline, and all patients (N = 5) with a maximum sustained coherence of less than 92% achieved an increase after theophylline; however, the overall increase (+8.8%, P = 0.087) was not significant. No theophylline-related adverse events were documented in 128 ECTs with theophylline, and no seizure exceeded 120 seconds. CONCLUSIONS: A calculated theophylline loading dose before ECT is well tolerated and effective in prolonging seizure duration and aiding with seizure generation in patients who do not seize readily. Its positive impact in patients with lower maximum sustained coherence, in addition to the potential existence of a dose-response relationship, should be further investigated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".