Bibliographic record
Abstract
Allow us to set the stage. You have a serious but common problem that has major emotional and financial implications for patients, caregivers, and society. There are a variety of therapeutic approaches, none of which has been extensively researched or studied “head-to-head.” You assemble a formidable team of experienced investigators and design a model study to compare three of the most highly recommended approaches against placebo. These investigators recruit a sufficient number of subjects and analyze the data appropriately. Finally, the results provide clinicians with firm, evidence-based recommendations for treatment. Sound too good to be true? Unfortunately, it is. The Alzheimer’s Disease Cooperative Study (ADCS) of the treatment of agitation in AD1 followed the plot exactly, right up to but not including the punch line. Behavioral and psychological symptoms in dementia (BPSD) are common, serious problems that affect the quality of life of both patient and caregiver, and frequently result in premature institutionalization. BPSD include agitation, aggression, delusions, hallucinations, depression, apathy, sleep disturbance, and sexually inappropriate behaviors. These difficulties occur in up to 90% of patients at some point in …
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.010 |
| 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".