Predictors of discharge destination from acute care in patients with traumatic brain injury
Bibliographic record
Abstract
INTRODUCTION: Many studies have assessed the predictors of morbidity/mortality of patients with traumatic brain injury (TBI) in acute care. However, with the increasing rate of survival after TBI, more attention has been given to discharge destinations from acute care as an important measure of clinical priorities. This study describes the design of a systematic review compiling and synthesising studies on the prognostic factors of discharge settings from acute care in patients with TBI. METHODS AND ANALYSIS: This systematic review will be conducted on peer-reviewed studies using seven databases including Medline/Medline in-Process, Embase, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, PsycINFO, CINAHL and Supplemental PubMed. The reference list of selected articles and Google Scholar will also be reviewed to determine other relevant articles. This study will include all English language observational studies that focus on adult patients with TBI in acute care settings. The quality of articles will be assessed by the Quality in Prognostic Studies tool. ETHICS AND DISSEMINATION: The results of this review will provide evidence that may guide healthcare providers in making more informed and timely discharge decisions to the next level of care for patient with TBI. Also, this study will provide valuable information to address the gaps in knowledge for future research. TRIAL REGISTRATION NUMBER: Trial registration number (PROSPERO) is CRD42016033046.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".