Scientific Impact and Clinical Influence: Identifying Landmark Studies in Burns
Bibliographic record
Abstract
Although many reviews describe significant advances in burn care, no studies have yet examined why these papers had such profound impact. Our objective was to identify the most highly cited, as well as the most clinically influential studies in burns, and describe their characteristics, to inform future research in the field. Web of Science was searched using keywords related to burns to identify the 100 most-cited burns papers. Study design, year and journal of publication, and subject of the paper were recorded. A mixed-methods approach was used to identify papers in burn research leading to change in clinical practice. Characteristics of these papers were compared with identify any factors predictive of future citations or clinical influence. The 100 highly cited papers were cited between 159 and 907 times. There was no correlation between total citations and journal impact factor, year of publication, or subject area. Level of evidence did not predict future citations or influence, but may be influenced by evolving research standards. Of 23 clinically influential studies, 6 were not among 100 most-cited. Using papers only from the 100 most-cited list was not sufficient to identify leading researchers in burns. Citation analysis is a beneficial, however not alone sufficient to identify landmark papers, particularly for multidisciplinary fields such as burns.
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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 | 0.081 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.070 | 0.073 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".