Bibliographic record
Abstract
A review of the top-cited articles in a scientific discipline can identify areas of research that are well established and those in need of further development, and may, as a result, inform and direct future research efforts. Our objective was to identify and characterize the top-cited articles in traumatic brain injury (TBI). We used publically available software to identify the 50 TBI articles with the most lifetime citations, and the 50 TBI articles with the highest annual citation rates. A total of 73 articles were included in this review, with 27 of the 50 papers with the highest annual citation rates common to the cohort of 50 articles with the most lifetime citations. All papers were categorized by their primary topic or focus, namely: predictor of outcome, pathology/natural history, treatment, guidelines and consensus statements, epidemiology, assessment measures, or experimental model of TBI. The mean year of publication of the articles with the most lifetime citations and highest annual citation rates was 1990 ± 14.9 years and 2003 ± 6.7 years, respectively. The 50 articles with the most lifetime citations typically studied predictors of outcome (34.0%, 17/50) and were specific to severe TBI (38.0%, 19/50). In contrast, the most common subject of papers with the highest annual citation rates was treatment of brain injury (22.0%, 11/50), and these papers most frequently investigated mild TBI (36.0%, 18/50). These findings suggest an intensified focus on mild TBI, which is perhaps a response to the dedicated attention these injuries are currently receiving in the context of sports and war, and because of their increasing incidence in developing nations. Our findings also indicate increased focus on treatment of TBI, possibly due to the limited efficacy of current interventions for brain injury. This review provides a cross-sectional summary of some of the most influential articles in TBI, and a bibliometric examination of the current status of TBI research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".