Diagnostic Imaging in Disasters: A Bibliometric Analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of diagnostic imaging in the clinical diagnosis, treatment, and follow-up management of patients in response to disasters. METHODS: A MEDLINE (OVID) search of original research articles identified 177 articles on this topic published since 2000. A bibliometric analysis was conducted on the top 100 articles ranked by average yearly citation. RESULTS: The most frequently studied disaster categories were disease outbreak (55 articles), armed conflict (23 articles), terrorist incident (10 articles), and earthquake (7 articles). The most studied disasters were the H1N1 influenza outbreak in 2009 (28 articles), Severe Acute Respiratory Syndrome outbreak in 2003 (24 articles), War in Afghanistan, 2001-2014 (8 articles), Iraq War, 2003-2011 (6 articles), and the Sichuan earthquake (China) in 2008 (6 articles). Among the first authors, 59 were primarily affiliated with Radiology. The United States of America produced the most articles (25 articles), followed by the People's Republic of China (24 articles). Eighty-one studies were retrospective, with 19 studies being prospective. Computed tomography was the most investigated modality (52.8%), followed by conventional radiography (33.3%) and ultrasound (9.7%). CONCLUSIONS: Our study identifies intellectual milestones in the utility of diagnostic imaging in response to various disasters, and could help guide future research in developing disaster management plans. (Disaster Med Public Health Preparedness. 2018;12:265-277).
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 | 0.014 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.216 | 0.212 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".