Progress in the Capture, Manipulation, and Delivery of Medical Media and Its Impact on Education, Clinical Care, and Research
Bibliographic record
Abstract
In this article, advances in the application of medical media to education, clinical care, and research are explored and illustrated with examples, and their future potential is discussed. Impact is framed in terms of the Sloan Consortium's five pillars of quality education: access; student and faculty satisfaction; learning effectiveness; and cost effectiveness. (Hiltz SR, Zhang Y, Turoff M. Studies of effectiveness of learning networks. In Bourne J, Moore J, ed. Elements of Quality Online Education. Needham, MA: Sloan-Consortium, 2002:15-45). The alternatives for converting analog media (text, photos, graphics, sound, video, animations, radiographs) to digital media and direct digital capture are covered, as are options for storing, manipulating, retrieving, and sharing digital collections. Diagnostic imaging is given particular attention, clarifying the difference between computerized radiography and digital radiography and explaining the accepted standard (DICOM) and the advantages of Web PACS. Some novel research applications of medical media are presented.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".