Bibliographic record
Abstract
Telemedicine has the potential substantially to improve the delivery of health care. However, cost-effectiveness studies are needed to help define the appropriate scope and application of telemedicine in different settings. Reports on the evaluation of telemedicine are dominated by technical and feasibility studies. Such studies may be very helpful for initial decision making. However, any cost information at this level tends to be very preliminary and often concerned with making a case to proceed further. Decision makers will wish for further information as the telemedicine application is introduced, to consider its effectiveness - its performance under routine conditions. Without information on the costs and effectiveness of telemedicine services, decision makers run the risk of supporting telemedicine systems that are not responsive to health-care needs or which do not provide cost-effective services. The most immediate needs seem to be improvements in the conduct and reporting of studies, and additional information on the performance of telemedicine in routine practice. Investigators need to provide transparent accounts of their studies, describing in detail the approaches taken, sources of data and assumptions made, and indicating the reliability of their results. Decisions may have to be made on the basis of limited studies, but sufficient detail must be made available to decision makers.
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 | 0.476 | 0.744 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.030 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.025 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 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".