Rehabilitation outcomes after central nervous system infections
Bibliographic record
Abstract
Methods: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with a literature search performed in databases such as PubMed, Scopus, Cochrane Library, and EMBASE.The search included articles in English without time restrictions and utilized appropriate combinations of keywords to ensure completeness.Clear inclusion and exclusion criteria were established, and the data were extracted and synthesized qualitatively.The quality assessment of the studies was performed using tools such as the Risk of Bias 2 (RoB 2) and the Newcastle-Ottawa Scale (NOS).Results: A total of 24 studies were included, evaluating the effectiveness of different rehabilitation approaches after CNS infections.Early mobilization and combined physiotherapy and speech therapy were found to be particularly effective in restoring functionality.The main prognostic factors for successful rehabilitation included early initiation of treatment and good general condition of the patient at the onset of rehabilitation.Conclusions: Rehabilitation after CNS infections requires a multidimensional approach, combining physiotherapeutic, occupational, and speech therapy interventions.The use of innovative technologies can enhance the effectiveness of rehabilitation.Timely diagnosis and therapeutic intervention, together with specialized rehabilitation approaches, contribute to better functional recovery and improved quality of life for 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".