Telemedicine – a scientometric and density equalizing analysis
Bibliographic record
Abstract
BACKGROUND: As a result of the various telemedicine projects in the past years a large number of studies were recently published in this field. However, a precise bibliometric analysis of telemedicine publications does not exist so far. METHODS: The present study was conducted to establish a data base of the existing approaches. Density-equalizing algorithms were used and data was retrieved from the Thomson Reuters database Web of Science. RESULTS: During the period from 1900 to 2006 a number of 3290 filed items were connected to telemedicine, with the first being published in 1964. The studies originate from 101 countries, with the USA, Great Britain and Canada being the most productive suppliers participating in 56.08 % of all published items. Analyzing the average citation per item for countries with more than 10 publications, Ireland ranked first (10.19/item), New Zealand ranked second (9.5/item) followed by Finland (9.04/item). The citation rate can be assumed as an indicator for research quality. The ten most productive journals include three journals with the main focus telemedicine and another five with the main focus "Information/Informatics". In all subject categories examined for published items related to telemedicine, "Health Care Sciences & Services" ranked first by far. More than 36 % of all publications are assigned to this category, followed by "Medical Informatics" with 9.72 % and "Medicine, General & Internal" with 8.84 % of all publications. CONCLUSION: In summary it can be concluded that the data shows clearly a strong increase in research productivity. Using science citation analysis it can be assumed that there is a large rise in the interest in telemedicine studies.
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
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 | Observational | high |
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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.066 | 0.081 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".