Evaluating the Development of a SPECT Protocol in a Canadian Epilepsy Unit
Bibliographic record
Abstract
BACKGROUND: Functional neuroimaging can address some challenges of seizure localization, and sometimes preclude the need for EEG recording using intracranial electrodes. Ictal Single Photon Emission Computed Tomography (SPECT) has developed into an important tool in the presurgical evaluation of patients with medically-intractable localization-related epilepsy. The purpose of the study was to determine whether the development of a programme using trained nurses to perform ictal injections enabled a more efficient delivery of radiopharmaceuticals and therefore a greater sensitivity and specificity of outcome. METHODS: In our epilepsy unit, nursing staff inject 99mTc-HMPAO at bedside, during or at seizure onset. Brain SPECT is performed later on a gamma camera. RESULTS: Since the implementation of the new protocol (February 2005), 57 scans have been performed: 22 ictal and 35 interictal. Latency of ictal injection was found to be 5-40 seconds (mean 19.7 sec, standard deviation (SD) 10.4). Only 20% of reconstituted radiopharmaceutical vials were not used. Contamination rate was nil. Sixty three percent of SPECT studies were concordant with standard presurgical evaluation. CONCLUSION: The latency of injections and the percentage of unused vials indicated an efficient and effective protocol compared to the literature. Our results show that ictal SPECT can be a safe, noninvasive procedure performed on a routine basis in the epilepsy unit when appropriately trained support staff are incorporated into a structured multidisciplinary programme.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".