Bibliographic record
Abstract
Hemorrhagic transformation (HT) is an ischemic stroke complication occurring in 10% of ischemic stroke patients with infarcts. HT is detected by symptom and CT monitoring, though exposes patients to repeated radiation doses. Symptom monitoring for detecting perioperative ischemia is ineffective in anesthetized patients. A non-ionizing modality for continuous monitoring of cerebral blood volume (CBV) would therefore be valuable. Electrical impedance tomography (EIT) is an imaging technique that reconstructs the internal conductivity distribution of a body non-invasively. EIT can detect changes in CBV, but only hemorrhage, not ischemia, has been detected with EIT. A novel EIT processing method was developed that depicted arterial blood pressure related CBV changes and successfully detected ischemia in swine, as verified by MRI. Software tools and EIT electrode hardware were developed to apply this method to functional imaging studies in humans. Finally, an automated electrode quality and data rejection tool, which simplifies EIT preprocessing, was developed. I would like to thank my supervisors Dr. Andy Adler and Dr. Rebecca Thornhill for their support and guidance throughout the making of this work. The feedback from Aaron Hill on the MIT Open-Courseware Python courses taken
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".