Treating cognitive impairments in primary central nervous system infections: A systematic review of pharmacological interventions
Bibliographic record
Abstract
BACKGROUND: This research synthesized scientific evidence on the use of pharmacotherapy as intervention to reduce cognitive impairments in adult patients with primary central nervous system (CNS) infections. METHODS: We searched for experimental studies published in English prior to October 2021 in MEDLINE, Embase and Cochrane databases. We included non-randomized studies (NRS) and randomized control trials (RCT) of pharmacotherapy versus placebo, drug, or a combination of drugs in adults with primary CNS infection. The certainty of the evidence was rated according to GRADE guidelines. RESULTS: We included 8 RCTs and 1 NRS, involving a total of 805 patients (50.77% male patients; mean age 42.67 ± 10.58) with Lyme disease (LD), herpes simplex virus type 1 (HSV-1), or Creutzfeldt-Jakob disease (CJD) studying the efficacy of antibiotics, antiviral, and non-opioid analgesic drugs, respectively. In patients with LD, antibiotics alone or in combination with other drugs enhanced certain cognitive domains relative to placebo. In patients with HSV-1, the results were inconsistent. In patients with CJD, flupirtine maleate enhanced baseline cognitive scores. The quality of RCT studies was low, and the quality of NRS of intervention was very low, suggesting low and very low certainty in the reported results. CONCLUSION: There is limited evidence and low certainty regarding the efficacy of antimicrobials and analgesics in reducing cognitive impairments in patients with LD, HSV-1, and CJD. Future efforts must be aimed at enhancing attention to clinical trial methodology and reporting, as well as reaching a consensus on outcome measures and the endpoint of clinical trials relevant to patients.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".