Bibliographic record
Abstract
Magnetic resonance imaging (MRI) techniques allow for significantly better imaging of the temporal lobe compared to computed tomography (CT) or other non-invasive modalities. For detection of foreign tissue lesions, MRI surpasses CT. For the highest non-invasive yield for detection of mesial temporal sclerosis, optimal sequences that should be employed are a heavily T1-weighted volumetric acquisition (to enable both volumetric calculation of hippocampal volume, and, if needed, intracranial volume), T2-weighted coronal sequences, with or without T2-mapping, fluid-attenuated inversion recovery (FLAIR) and, to exclude subtle susceptibility effects from hematoma or cavernoma, gradient echo scans. Magnetic resonance spectroscopy (MRS) may show a decrease in N-acetyl aspartate (NAA) concentration, or NAA: Choline + creatine ratio. Functional MRI is a new and exciting tool that offers the promise of accurately localizing hemispheric functions; its role in the preoperative evaluation of temporal lobe seizures remains uncertain at present.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".