General intensive care for patients with traumatic brain injury: An update
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a growing epidemic throughout the world and may present as major global burden in 2020. Some intensive care units throughout the world still have no access to specialized monitoring methods, equipments and other technologies related to intensive care management of these patients; therefore, this review is meant for providing generalized supportive measurement to this subgroup of patients so that evidence based management could minimize or prevent the secondary brain injury. METHODS: Therefore, we have included the PubMed search for the relevant clinical trials and reviews (from 1 January 2007 to 31 March 2013), which specifically discussed about the topic. RESULTS: General supportive measures are equally important to prevent and minimize the effects of secondary brain injury and therefore, have a substantial impact on the outcome in patients with TBI. The important considerations for general supportive intensive care unit care remain the prompt reorganization and treatment of hypoxemia, hypotension and hypercarbia. Evidences are found to be either against or weak regarding the use of routine hyperventilation therapy, tight control blood sugar regime, use of colloids and late as well as parenteral nutrition therapy in patients with severe TBI. CONCLUSION: There is also a need to develop some evidence based protocols for the health-care sectors, in which there is still lack of specific management related to monitoring methods, equipments and other technical resources. Optimization of physiological parameters, understanding of basic neurocritical care knowledge as well as incorporation of newer guidelines would certainly improve the outcome of the TBI 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".