The effect of pre-hospital intubation on prognosis in infants, children and adolescents with severe traumatic brain injury
Bibliographic record
Abstract
INTRODUCTION: Traumatic brain injury is one of the leading causes of death and sources of heavy societal burden. Hypoxemia and hypercapnia are the 2 common complications of brain injury. Intubation seems to be an effective intervention for preventing the 2 complications in pre-hospital setting. But the results of the existing studies on the effect of pre-hospital intubation on prognosis of patients (aged less than 18) with severe traumatic brain injury are conflict. Thus, in this study, we aim to conduct a systematic review and meta-analysis to evaluate whether pre-hospital intubation is benefit for the prognosis in infants, children and adolescents with severe traumatic brain injury. METHODS: We will develop a systematic search strategy which includes MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Chinese Biomedical Literature Database, WanFang Data and China National Knowledge Infrastructure. The WHO International Clinical Trials Registry Platform will be searched for the ongoing studies as well. The cohort studies which aim to evaluate the effect of pre-hospital intubation for infants, children and adolescents with severe traumatic brain injury will be selected. The Newcastle-Ottawa Scale will be used for assessing the risk of bias of the included studies. RESULTS: The results of this study will be presented in the full-text of the systematic review. CONCLUSION: This is the first systematic review and meta-analysis about evaluation of the effect of pre-hospital intubation on prognosis in infants, children and adolescents with traumatic brain injury. PRESPERO REGISTRATION NUMBER: CRD42019121214.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".