The human immunosenescence phenotype: does it exist?
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
"Immunosenescence" has been invoked as the root cause of increased incidence and severity of infectious disease in older adults and their poorer response to vaccination, and is implicated in increased solid cancers and increased autoimmunity with age. But how to define it in the individual and to show that immunosenescence is responsible for these adverse health outcomes? How can we monitor interventions aimed at restoring appropriate immune function to overcome these perceived immune deficits? Hence, the many efforts over the years aimed at establishing biomarkers of immunosenescence which to be useful must exhibit robust correlations with the chosen clinical outcome. Developments in "omics" technologies acquiring unprecedently detailed data on personal trajectories of immunosenescence and taking into account the under-appreciated importance of gender, ethnicity geography, socioeconomic, and multiple other differences will be of pivotal importance to identify biomarkers that are clinically useful at the level of the individual. This contribution addresses the question of whether or not we are currently in possession of any such useful biomarkers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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