Bibliometric analysis of research on emergency medicine and ultrasound-guided intubation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To examine the historical significance, current trends, impact assessment, prominent authors, participating institutions, and sources of funding, among other aspects, through scholarly articles. Methodology: The emergency medicine and ultrasound-guided intubation (EMUGI)-related publications were extracted from the Scopus database. Relevant search terms were built using the MeSH database. Lotka's law for productivity, Bradford's law for literary sources, thematic mapping for intellectual structure, and developing themes were used in analysis. Results: With more scholarly activity and a spike in interest following 2014, EMUGI research has seen a notable expansion. Cooperative efforts and international partnerships were seen with Canada and Spain. Lotka's law analysis showed that although many writers publish only one article for EMUGI research, a small number of well-known writers contribute significantly. Bradford's law draws attention to a concentrated publishing scene, including a core group of esteemed journals. Highly referenced papers in EMUGI research covered a range of subjects, thus demonstrating their influence. Keywords that were particularly relevant in emergency medicine include "ultrasound," "ultrasonography," and "COVID-19." The thematic development of EMUGI research revealed changing priorities and newly arising subjects, including bleeding, prenatal diagnosis, and cardiac arrest. Conclusion: Thematic mapping aggregates the study participants Ultimately, EMUGI research grew significantly, underlining cooperative efforts, important publications, and developing research topics.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.097 | 0.152 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it