Bibliographic record
Abstract
In recent years, tau immunotherapy has advanced from proof-of-concept studies [Sigurdsson EM, NIH R01AG020197, 2001; Asuni AA, et al: J Neurosci 2007;27:9115-9129], which have now been confirmed and extended by us and others. Phase I clinical trials on active and passive tau immunizations are being conducted, with several additional passive tau antibody trials likely to be initiated in the near future for Alzheimer's disease and other tauopathies. Because tau pathology correlates better with the degree of dementia than amyloid-β (Aβ) pathology, greater clinical efficacy may be achieved by clearing tau than Aβ aggregates in the later stages of the disease, when cognitive impairments become evident. Substantial insight has now been obtained regarding which epitopes to target, mechanism of action and potential toxicity, but much remains to be clarified. All of these factors likely depend on the model/disease or stage of pathology and the immunogen/antibody. Interestingly, tau antibodies interact with the protein both extra- and intracellularly, but the importance of each site for tau clearance is not well defined. Some antibodies are readily taken up into neurons, whereas others are not. It can be argued that extracellular clearance may be safer but less efficacious than intraneuronal clearance and/or sequestration to prevent secretion and further spread of tau pathology. Development of therapeutic tau antibodies has led to antibody-derived imaging probes, which are more specific than the dye-based compounds that are already in clinical trials. Such specificity may give valuable information on the pathological tau epitope profile, which could then guide the selection of therapeutic antibodies for maximal efficacy and safety. Hopefully, tau immunotherapy will be effective in clinical trials, and further advanced by mechanistic clarification in experimental models with insights from biomarkers and postmortem analyses of clinical subjects.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.023 |
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".