Patient Communication: Talking the talk: new online interpretation system
Bibliographic record
Abstract
An innovative interpretation service that allows medical staff to converse directly with non-English speaking patients at New York's Bellevue Hospital Center is proving to be cost-effective and efficient, an International Conference on Urban Health was told. The remote simultaneous medical interpretation system is more accurate than other types of interpretation, said Dr. Francesca Gany, director of the Center for Immigrant Health at the New York University School of Medicine. The interpretation service, which uses voice-over-Internet technology, is a world first. The health professional and patient each wear a headset attached to an Internet protocol phone. The phone connects them to interpreters, who translate as they speak. “It's our hope that this will become global,” Gany told conference participants in New York last fall. The service, which costs about US$1 a minute, will soon be available through the New York hospital system. An error analysis done by Gany's centre found simultaneous translation to be more accurate than over-the-phone consecutive translation, or consecutive translation by a trained translator in the same room. In those situations, translators often forget to repeat something because of the time lapse, she said. The centre has also initiated a randomized controlled trial, comparing the new system with “usual and customary” ones, considering aspects such as patient adherence and test ordering. “There can be a hidden cost of not using [well qualified] interpreters, such as a tendency to order more tests,” Gany observed. — Ann Silversides, Toronto
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".