Bibliographic record
Abstract
Biomarkers are required to improve our diagnostic sensitivity and specificity and to monitor the biological activity of the Alzheimer's disease (AD) in terms of the burden of neural involvement and the tempo of disease progression. Biomarkers will initially supplement our more traditional neuropsychological and imaging markers but may eventually evolve into useful surrogate endpoints in AD research. These markers may also provide important mechanistic clues to the pharmacological action of anti-dementia compounds. At this point, the combination of elevated cerebrospinal fluid phosphorylated TAU (CSF p-TAU) proteins and low CSF ABeta(1-42) are the only biomarkers with the sensitivity and specificity to serve as useful diagnostic biomarkers capable of distinguishing AD from other dementias in the early stages. Advances in non CSF tests is urgently required. Markers assessing the progression of disease do not necessarily require the same high disease specificity as diagnostic markers, but need to be sensitive to changes in disease state. At present, candidate markers fall under four main biological rationales: (1) Specific markers of AD neuropathology; (2) Non-specific markers of neural degeneration; (3) Markers of oxidative stress; (4) Markers of neural inflammation. It is foreseeable that a panel of such markers might prove advantageous. It will be important to develop "non-invasive" markers utilizing readily obtainable tissue samples such as serum or urine to monitor disease progression (or hopefully regression). Repeated sampling would allow for comparison with traditional neuropsychological and imaging measures. The assays themselves will need to be reproducible, reliable and relatively inexpensive. Unfortunately, these biomarkers are in the formative stages of testing and results at present are inconclusive. To facilitate biomarker development in the future it would be highly advantageous to begin to collect and store biological specimens as an adjunct to current research in AD.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".