Bibliographic record
Abstract
A few months ago, I was helping my daughter, a senior in high school, with her history thesis paper. In it, she argued that the discovery and use of antibiotics were the true heroes during World War II, having saved millions of lives to this day. In a similar manner, novel imaging techniques used today are changing the way that we confront and approach the diagnosis of infectious diseases in the brain and spine. Currently, most of the brain and spinal infections are diagnosed with the use of magnetic resonance imaging (MRI). With this technology, it is relatively easy to demonstrate a brain abscess using diffusion-weighted imaging or to demonstrate abnormal meningeal enhancement suggestive of meningitis. However, due to multiple infectious pathogens such as parasites, bacteria, fungus, viral diseases, and prion infections, the differential diagnosis is broad. Because of this, the necessity to obtain an early diagnosis is crucial, because symptoms can appear suddenly and progress into brain damage, paralysis, or death in some cases. The goal of this issue is to cover all of these entities, showing the most important topics in MRI. To accomplish this, I have had the good fortune to work with an excellent group of neuroradiologists from Latin America, India, Canada, and the United States. Because this is an extensive topic, we have decided to present it in 2 issues; the first 4 articles will cover pediatric intracranial infections, central nervous system tuberculosis, neurocysticercosis, and fungal infections of the brain. The following 4 articles will cover meningitis and ventriculitis, MRI of the brain in the patient with HIV, brain MRI in viral diseases and prion diseases, and finally, MRI of spinal infections. I hope that these issues will be informative and helpful, providing current and updated information for the diagnosis of brain and spinal infections using MRI. I would like to thank my friend, Scott W. Atlas, for the great privilege and the invitation to host these 2 issues related to the important topics of “MRI in Brain and Spinal Infection.” At the same time, I would like to express my sincere gratitude to the superb group of authors for their effort and time preparing all the manuscripts and material for these 2 issues. I want to thank Julie Chase, Editorial Coordinator from Wolters Kluwer Health Medical Research, Lippincott Williams and Wilkins, Ovid Technologies, for her guidance and assistance. I also wish to thank some close collaborators at Beth Israel Deaconess Medical Center in Boston—David Hackney, Gul Moonis, Rafeeque Bhadelia, Douglas Teich, Jonathan Kleefield, Nagamani Peri, and Alice Fisher—for all their guidance and for sharing their expertise with me. Finally, I want to say thanks to my lovely wife, Irina, and my wonderful kids for all their support.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".