Sources of Variability in the Detection of Cerebral Emboli with Transcranial Doppler During Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVE: The application of intensity thresholds for embolus detection with transcranial Doppler (TCD) can exclude from analysis an unrecognized proportion of high-intensity transient signals (HITS))whose intensities are below the threshold. The lack of consistent threshold criteria between clinical trials may explain part of the discrepancy in the reported HITS counts. We investigated the effect of choosing different thresholds on the sensitivity and specificity of detecting HITS during cardiopulmonary bypass (CPB). METHODS: Two observers independently analyzed TCD recordings from 8 patients under CPB. Doppler signals were classified as true HITS, equivocal HITS, artifacts, and Doppler speckles according to preestablished criteria. The relative intensity of Doppler signals was measured by two different methods (TCD software vs manual). Receiver Operating Characteristic curves determined the optimal threshold for each of the two intensity methods. RESULTS: Reviewers achieved agreement in 96% of 2190 Doppler signals (kappa = 0.90). Relative intensities calculated with the TCD-software method were 3 dB (95% CI: 3.0-3.4) higher than the manual method. The optimal threshold was found at 10 dB (sensitivity: 99%; specificity: 90.8%) with the software method and at 7 dB with the manual method (sensitivity: 96%; specificity: 83%). The use of an intensity threshold 2 dB higher than the optimal increased the rejection of true HITS by 8% and 14%, respectively. CONCLUSIONS: Using intensity thresholds higher than the optimal for embolus detection decreases HITS counts. Choosing a threshold depends on the type of method used for measuring the signal intensity. Uniform threshold criteria and comparative studies between different Doppler devices are necessary for making clinical trials more comparable.
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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 | 0.049 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".