What are the demographic and clinical differences between those older adults with traumatic brain injury who receive a neurosurgical intervention to those that do not? A systematic literature review with narrative synthesis
Bibliographic record
Abstract
OBJECTIVES: This review aimed to identify the demographic and clinical differences between those older adults admitted directly under neurosurgical care and those that were not, and whether EMS clinicians could use these differences to improve patient triage. METHODS: The authors searched for papers that included older adults who had suffered a TBI and were either admitted directly under neurosurgical care or were not. Titles and abstracts were screened, shortlisting potentially eligible papers before performing a full-text review. The Newcastle-Ottawa Scale was used to assess the risk of bias. RESULTS: A total of nine studies were eligible for inclusion. A high abbreviated injury score head, Marshall score or subdural hematoma greater than 10 mm were associated with neurosurgical care. There were few differences between those patients who did and did not receive neurosurgical intervention. CONCLUSIONS: Absence of guidelines and clinician bias means that differences between those treated aggressively and conservatively observed in the literature are fraught with bias. Further work is required to understand which patients would benefit from an escalation of care and whether EMS can identify these patients so they are transported directly to a hospital with the appropriate services on-site.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".