Bibliographic record
Abstract
Objective: To determine if iPads using "Face Time" technology could improve acute stroke care in rural communities. Background: Telestroke has been used for some years. However, typically this has involved the use of dedicated and expensive telehealth equipment or facilities. Smaller rural health facilities may only infrequently see stroke patients, and therefore may not have telehealth capability. iPads (Apple Inc.) are inexpensive, and provide an easy method to provide video-conference capability. Additionally, they are portable,and can be used with a cellular or WiFi signal connection. We studied a group of rural hospital emergency departments connected to a tertiary centre hospital neurology department using iPads. Methods: Five rural hospitals and five tertiary hospital neurologists were all supplied with iPads (Apple, Inc.). If a stroke patient presented, the neurologists had the opportunity to perform an immediate video-conference with the patient and family, and the emergency doctor. Results: Over six months, there were 26 iPad video-conferences between rural hospital emergency departments and tertiary neurologists. Conclusions: Not all calls to neurologists regarding stroke patients required video-conference. In 23 of 26, an improvement or change in care was judged to result from the addition of video-conference capability. The ability to use immediate, portable, inexpensive video-conference in the assessment of rural stroke patients is practical and beneficial.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".