Bibliographic record
Abstract
The past few decades have seen phenomenal growth in medical imaging technologies and their impact in biomedicine. This trend will continue, and indeed expand with the development of new techniques. However, another development likely to have major importance is the development of systems offering combined modality imaging in the same platform. Such hybrids include PET/CT, PET/MRI, SPECT/CT, and x-ray/MR systems, and offer not only the convenience of access to multiple modalities in the same session, but more importantly, synergistic uses in which the combined modality is more than the simple sum of previous methods. A simple example is the use of CT data for attenuation correction in PET and SPECT imaging, which is greatly facilitated in a hybrid system. In our own work, we have developed a hybrid x-ray/MR system for image guided interventions. It brings together the high spatial and temporal resolution of x-ray fluoroscopy with the 3D imaging capabilities, soft tissue contrast, and sensitivity to physiological processes of MRI. System development required exploration of a number of interesting problems, including the effect of high magnetic fields on x-ray tubes. The system has been used for several diagnostic and minimally invasive applications, including biopsies, arthrograms, and TIPS procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".