Bibliographic record
Abstract
PURPOSE OF REVIEW: In this article we aim to review the recent literature concerning the management of traumatic brain injury patients, summarize the main findings, and discuss the impact of these findings on clinical practice. RECENT FINDINGS: Several authors have focused on the development of more reliable and informative tools to predict outcome in traumatic brain injury as well as refining the definition of cerebral ischemia in last year's literature. The validity of the current cerebral perfusion pressure management guidelines has also come under scrutiny. It appears that a one size fits all therapy is not a suitable approach for traumatic brain injury patients. An individualized approach, depending on the integrity of pressure autoregulation mechanisms, would be more advisable. Clinical trials investigating brain protective treatments in head injured patients have been disappointing so far. Increasing the homogeneity of patients entering brain protective studies might be an answer. Finally, the use of hyperoxia as well as factors contributing to secondary brain injury such as the occurrence of hyperthermia, with or without an infectious process, have been assessed in head injury patients. SUMMARY: The key term for the management of traumatic brain injury patients in the early twenty-first century will clearly be 'individualized therapy'. The search of an ideal cerebral perfusion pressure target that would fit every head-injured patients is a utopia. More energy should be focused on the development of reliable tools for outcome prediction and outcome assessment for traumatic brain injured patients. That, and a better targeting of patients entering brain protective trials, should increase the likelihood of demonstrating a significant salvaging effect of a particular treatment in humans.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".