Subject Index Vol. 22, 2003
Bibliographic record
Abstract
Acoustic neurinoma 124 -neuroma 130 Age-adjusted death rate 179 -risks 290 Aging 23, 315 Alcohol 297, 338 Alzheimer's disease 1, 13 Amyotrophic lateral sclerosis 217, 229 Anticoagulation 118 Apolipoprotein E 13 Arabs, multiple sclerosis 82 Aseptic meningitis 344 Aspirin 106 Atherosclerosis 37 A4T mutation 235 Atrial fibrillation 118, 204 Availability, neurological inpatient services 255 Bereavement 211 Brain cancer 46, 130 -stem 275 Brazil, mortality trends 179 Cadaveric dura mater 57 Campylobacter jejuni 245 Canada, multiple sclerosis 75 Cardioembolic stroke 204 Case-control studies 217, 239 Cellular telephones 124 Central/Eastern Europe, neurological inpatient services 255 Cerebral angiography 106 -infarction, acute 31 Cerebrovascular disease 179, 275 Chile, alcohol 338 Cholesterol 331 Cigarette smoking 297 Coffee 297 Cognition 23 Cognitive decline 165 -impairment 172, 315, 325 Cognitively impaired not demented 265 Correlation analysis 87 Cox proportional hazards model 290 Creutzfeldt-Jakob disease 57 CTG trinucleotide repeat 283 CYP2D6 357 Cysticercosis 139 Cytochrome P-450 IID6 356
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.497 | 0.494 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".