Bibliographic record
Abstract
Abstract The diagnosis of neurological disease (ND) traditionally involves the association of specific symptoms in a patient with a clinical examination, consideration of the patient's prior medical history, and clinical laboratory testing (e.g., electroencephalography, electromyography). The decoding of the human genome has made possible the identification of proteomic and genomic biomarkers associated with specific neurological disorders. The identification of multiple biomarkers, including determination of chemical nature and concentration, provides a disease “signature” that is not achievable through testing for any single marker alone. Biomarker signatures can augment conventional clinical evaluation and testing by improving diagnosis, prognosis, monitoring progression, and management of ND. Biomarker signature profiling for a patient can allow for personalized medicine catered to individual need. The need for biosensors in the detection of ND is illustrated by recent events. Two cases of bovine spongiform encephalopathy (BSE) in Canada have cost their beef industry millions of dollars and resulted in precluding shipment of cattle from Canada to the United States. The ability to determine absence of prion disease in live cattle could greatly increase confidence in cattle imports. In a separate example, Tysabri, an immunosuppressant drug effective in lengthening periods of remission in patients with multiple sclerosis (MS), was withdrawn from the market because of occurrence of progressive multifocal leukoencephalopathy (PML) in treated individuals. PML in the treated MS individuals was caused by proliferation of JC virus. The ability to detect JC virus production during treatment with Tysabri could remedy this problem. Biosensors designed to detect analytes indicative of disease are currently available. Ranging from single target detection to more complex multitarget signature profiling, these biosensors are offered for a wide range of disorders. Many focus on the detection of cancer and ND. The platforms used for cancer detection are directly applicable to ND determination because the sensors detect analytes or genetic mutations present in both disease types. Specific products and technologies that are already being used for or can be readily adapted to ND diagnosis, prognosis, and treatment are discussed. A select list of ND‐related biomarkers for current and future study is presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".