La tomographie par émission de positons à l'étude de la réponse hémodynamique temporelle induite par activation cérébrale (TEP-RHETIAC)
Bibliographic record
Abstract
The brain can be explored while a subject executes different tasks. Such techniques are commonly referred as activation studies. The regions of the brain involved in a particular task can be located by comparing regional differences in tracer concentration to a control state. Group of neurons in the brain form complex logical circuits and their activities increase while they interpret a stimulation. These neuronal activities require glucose and oxygen and these substances travel in the blood following hemodynamic rules. Many factors influence the hemodynamic response induced by brain activation (FRIBA), but certain aspects of its temporal behavior are still unclear. A special technique was implemented with an ECAT EXACT HR+ (CTI/Siemens) to fulfil this lack in PET activation studies. The new HR+ has this 3D option, which can help to evaluate the FRIBA with a tracer (11C-CO) that remains in the blood vessels to measure the cerebral blood volume (CBV) differences. Only CBV studies can appreciate the temporal course of the FRIBA in PET. The resulting images are, however, very noisy and many pitfalls are present during their analysis. (Abstract shortened by UMI.)
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".