Enhanced transcranial Doppler procedure for the Third World
Bibliographic record
Abstract
Transcranial Doppler Ultrasound (TCD) measures cerebral blood flow velocities, and is commonly used to examine the arteries of the Circle of Willis in patients suspected of suffering from severe blood flow disruption to the brain. Diagnostic tools such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) are readily accessible in developed countries but in developing countries the cost is well beyond the means of many. Likewise the expense of in-patient care is excessive in developing countries: hence some trained professionals run clinics from their homes. Due to its low costs and portability, ultrasound is the most common imaging modality for clinical diagnosis and monitoring of symptoms. TCD is therefore critical for neurological healthcare in developing countries. However, finding a suitable site (an acoustic window) to perform TCD can be very difficult or even impossible, often resulting in a time-consuming procedure to first locate an adequate site. The aim of this research is to reliably detect the optimum transtemporal acoustic window for TCD from a preliminary B-Mode (image) ultrasound scan. Once the optimized location has been established a TCD can commence at the predefined location. Three sheep heads were examined (left and right) with a Sonix RP (Ultrasonix, Canada) and raw radio frequency data was collected. The quantitative Broadband Spectral Difference (BSD) technique for attenuation-velocity was used to identify the temporal bone. The quantitative temporal bone detection methods give an alternative to blindly searching for the transtemporal acoustic window with a TCD scan by trial and error.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".