Bibliographic record
Abstract
In the unconscious patient, there is a diagnostic void between the neurologic physical exam, and more invasive, costly and potentially harmful investigations. Transcranial color-coded sonography and two-dimensional transcranial Doppler imaging of the brain have the potential to be a middle ground to bridge this gap for certain diagnoses. With the increasing availability of point-of-care ultrasound devices, coupled with the need for rapid diagnosis of deteriorating neurologic patients, intensivists may be trained to perform point-of-care transcranial Doppler at the bedside. The feasibility and value of this technique in the intensive care unit to help rule-in specific intra-cranial pathologies will form the focus of this article. The proposed scope for point-of-care transcranial Doppler for the intensivist will be put forth and illustrated using four representative cases: presence of midline shift, vasospasm, raised intra-cranial pressure, and progression of cerebral circulatory arrest. We will review the technical details, including methods of image acquisition and interpretation. Common pitfalls and limitations of point-of-care transcranial Doppler will also be reviewed, as they must be understood for accurate diagnoses during interpretation, as well as the drawbacks and inadequacies of the modality in general.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".