Providing predictive testing for Huntington disease via telehealth: results of a pilot study in British Columbia, Canada
Bibliographic record
Abstract
Predictive testing (PT) for Huntington disease (HD) usually requires several in-person appointments which acts as a barrier to testing for those from remote regions. This pilot study reports the use of telehealth PT to examine whether such telehealth testing improves access to HD PT while maintaining quality of care and support. Individuals underwent PT via the telehealth protocol or standard in-person protocol and were asked to complete surveys regarding their experience. Results reveal no significant differences between the in-person-tested and telehealth-tested groups with respect to quality of care, information, counselling and support. The majority of participants in both groups stated that pre-test counselling had provided them with sufficient knowledge about the advantages and disadvantages of undergoing testing, the opportunity to ask questions, and the ability to make an informed decision. The majority of participants in both groups were satisfied by the manner in which results were delivered and stated they had received sufficient information regarding the implications of these results. This study reveals that telehealth PT improves access while maintaining quality of care and support.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.025 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".