Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it