The 60 most highly cited articles published in the Journal of Telemedicine and Telecare and Telemedicine Journal and E-health
Bibliographic record
Abstract
We analysed the most highly cited articles in two specialist telemedicine journals, the Journal of Telemedicine and Telecare (JTT) and Telemedicine Journal and E-health (TJEH). Articles were extracted from the Science Citation Index Expanded in September 2012. A total of 1810 articles were listed for the JTT and 1550 for TJEH. In the JTT, the mean number of citations was 43 (SD 13); in TJEH the mean number of citations was 30 (SD 11). The average number of citations for the JTT was significantly higher than for TJEH (P < 0.001). In each journal, the 60 articles which had the most citations were identified as highly cited publications (HCPs). The 60 HCPs in the JTT originated from 16 countries; the 60 HCPs in TJEH originated from 10 countries. Considering both journals together, the majority of HCPs came from the US, UK, Australia and Canada. In the JTT, the mean number of authors for each HCP was 4.6 (SD = 3.1); in TJEH, the mean number of authors for each HCP was 4.5 (SD = 2.3). There was no difference between the two journals (P = 0.84) and the characteristics of the HCPs published in the JTT and TJEH were broadly similar. Although HCPs are not a direct method of measuring quality, they are an indicator of the scientific impact of the articles.
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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.073 | 0.084 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".