A systematic review of drug interventions to enhance cognition in patients with primary central nervous system infections
Bibliographic record
Abstract
Abstract Background Although drug therapy is part of the clinical routine for individuals with central nervous system (CNS) infections, its efficacy with respect to cognitive impairments has not been systematically studied. We aimed to synthesize the evidence for optimal treatment decisions. Method We searched for experimental studies published in English prior to October 2021 in MEDLINE, Embase and Cochrane databases. We selected non‐randomized studies (NRS) and randomized control trials (RCT) of a drug efficacy versus placebo, another drug, or a combination of drugs. The certainty of the evidence was rated according to GRADE guidelines. Result We included eight RCTs and one NRS, involving a total of 805 patients (50.77% male patients; mean age 42.67±10.58; duration (in years) of primary CNS infections 4.69±3.16) 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. The duration of the interventions varied, depending on the type of CNS infection and class of drug, with a range between four weeks in patients with LD treated with antibiotics and 18 weeks in patients with HSV‐1 treated with an antiviral drug. 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. Of the available evidence, only one study had “low” risk of bias. An important limitation of the included studies was the lack of consistent reporting of adverse effects and safety parameters, which must be addressed in future work. Conclusion There is not enough existing evidence of sufficient quality to support the use of antimicrobials and non‐opioid analgesics in combatting cognitive deficits in patients with LD, HSV‐1, or CJD. Future clinical trials enrolling participants at earlier stages of primary CNS infections, at the time of greatest inflammation, and which address the elements of biases found in the existing clinical trials, are warranted.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".