Minocycline benefits negative symptoms in early schizophrenia: a randomised double-blind placebo-controlled clinical trial in patients on standard treatment
Bibliographic record
Abstract
The onset and early course of schizophrenia is associated with subtle loss of grey matter which may be responsible for the evolution and persistence of symptoms such as apathy, emotional blunting, and social withdrawal. Such 'negative' symptoms are unaffected by current antipsychotic therapies. There is evidence that the antibiotic minocycline has neuroprotective properties. We investigated whether the addition of minocycline to treatment as usual (TAU) for 1 year in early psychosis would reduce negative symptoms compared with placebo. In total, 144 participants within 5 years of first onset in Brazil and Pakistan were randomised to receive TAU plus placebo or minocycline. The primary outcome measures were the negative and positive syndrome ratings using the Positive and Negative Syndrome Scale. Some 94 patients completed the trial. The mean improvement in negative symptoms for the minocycline group was 9.2 and in the placebo group 4.7, an adjusted difference of 3.53 (s.e. 1.01) 95% CI: 1.55, 5.51; p < 0.001 in the intention-to-treat population. The effect was present in both countries. The addition of minocycline to TAU early in the course of schizophrenia predominantly improves negative symptoms. Whether this is mediated by neuroprotective, anti-inflammatory or others actions is under investigation.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".