The Impact of Intravenous Lidocaine on ICP in Neurological Illness: A Systematic Review
Bibliographic record
Abstract
Background. The goal of our study was to perform a systematic review of the literature to determine the effect that intravenous (IV) lidocaine had on ICP in patients with neurological illness. Methods. All articles are from MEDLINE, BIOSIS, EMBASE, Global Health, Scopus, Cochrane Library, the International Clinical Trials Registry Platform (inception to March 2015). The strength of evidence was adjudicated using both the Oxford and GRADE methodology. Results. Ten original articles were considered for the final review. There were 189 patients studied. Seven studies focused on prophylactic pretreatment with IV lidocaine to determine if there would be an attenuation of ICP spikes during stimulation, with 4 displaying an attenuation of ICP. Three studies focused on a therapeutic administration of IV lidocaine in order to determine ICP reduction effects. All therapeutic studies displayed a reduction in ICP. Conclusions. We cannot make a strong definitive recommendation on the effectiveness of IV lidocaine on the attenuation of ICP spikes during stimulation. There currently exists both Oxford 2b and GRADE B literature to support and refute the attenuation of ICP spikes with IV lidocaine during stimulation. There currently exists Oxford 2b, GRADE B evidence to support ICP reduction with lidocaine when used as a therapeutic agent.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| 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) | 0.004 | 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".