Methodological Issues in Negative Symptom Trials
Bibliographic record
Abstract
Individuals from academia, the pharmaceutical industry, and the US Food and Drug Administration used a workshop format to discuss important methodological issues in the design of trials of pharmacological agents for improving negative symptoms in schizophrenia. The issues addressed included the need for a coprimary functional measure for registration trials; the characteristics of individuals who should enter negative symptom trials; the optimal duration for a proof-of-concept or registration trial; the optimal design of a study of a broad-spectrum agent that treats both positive and negative symptoms or a co-medication that is added to an antipsychotic; the relative strengths and weaknesses of available instruments for measuring negative symptoms; the definition of clinically meaningful improvement for these trials; and whether drugs can be approved for a subdomain of negative symptoms.
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 | 0.836 | 0.889 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".