Bibliographic record
Abstract
As Alzheimer's disease remains a clinical diagnosis, and as clinical diagnosis can be difficult, it makes sense to look for so-called biomarkers. A biomarker predicts who is likely to have the illness and who is not. Some biomarkers might even correlate with a clinically meaningful response to treatment. Developing biomarkers is often characterized as searching for a diagnostic gold standard that can seem appealing in its promise of certainty. Even so, considering both the economic history of the gold standard and the results of neuropathological studies, framing the search for measurable, biological correlates of dementia syndromes in this way is likely to be self-defeating. Instead of considering biomarkers as providing certainty through referent criterion validation, currently it makes more sense to test their construct validity and their predictive ability. This means that while biomarkers should inform, they will not dictate clinical meaningfulness. For the foreseeable future, even were they to inform diagnosis, biomarkers cannot substitute for understanding whether patients and caregivers find a given dementia treatment effective. Instead, clinicians should recognize their own determining role, both in dementia diagnosis and in the evaluation of treatment. These roles will best be executed by hearing what patients and caregivers tell us about dementia, and its response to treatment.
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.
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 | 0.110 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.015 | 0.041 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".