Nursing analysis and effectiveness of lateral ventricle catheter drainage sustained ventricular perfusion combined with lumbar drainage in treating intracranial infection
Bibliographic record
Abstract
Objective To analyze the effect and nursing of lateral ventricle catheter drainage sustained ventricular perfusion combined with lumbar drainage in the treatment of intracranial infection, to evaluate the effect. Methods 60 patients with intracranial infection in the General Hospital of Shenyang Military Region from November 2012 to October2013 were selected. The clinical data of temperature, peripheral hemogram, cerebrospinal fluid biochemical indicators were analyzed. Canada neural function assessment scale(CNS) was used to evaluate the neurologic prognosis. Homemade nursing satisfaction questionnaire was used to investigate the patients' satisfaction. Results ①1 patient died, 3 patients were vegetative state, 1 patient was hydrocephalus. The temperature before the treatment [(38.0±0.5) ℃] was higher than that after the treatment [(36.4±1.0) ℃], the difference was statistically significant(P 0.05). The proportion situation of peripheral blood WBC and cerebrospinal fluid WBC after the treatment were better than those before the treatment, the differences were statistically significant(P 0.05). ②The CNS scores after the treatment [(33.46±4.20) scores] were higher than those before the treatment [(17.40 ±3.79) scores], the difference was statistically significant(P 0.05).③45 patients with great satisfaction, 10 patients with basic satisfaction were found and no dissatisfaction was found according to the satisfaction analysis after treatment. Conclusion The lateral ventricle catheter drainage sustained ventricular perfusion combined with lumbar drainage can effectively treat the patients with intracranial infection, and promote the recovery of the patient, it is right to promote in clinical application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".