N02 Providing predictive testing via telehealth to improve access to predictive testing for HD: results of a pilot study
Bibliographic record
Abstract
Background Predictive testing (PT) for HD requires several in-person appointments. A previous interview study of individuals at risk for HD in British Columbia (BC), Canada revealed that the accessibility of PT can be a barrier for two major reasons: distance and the inflexibility of the testing process. Based on the results of this study, coupled with expert consultation, an effective and practical portable telehealth testing protocol was developed, including an informational website and locally supported telehealth appointments. Aims The objective of this project was to conduct a pilot project to examine whether this telehealth protocol can improve access to HD PT while maintaining quality of care and support for those undergoing the process. Methods Consented individuals underwent PT via the telehealth protocol and were asked to complete surveys throughout the testing process to capture several important factors including: overall experience of the telehealth process, information and understanding, support and accessibility of care. Results A total of 29 individuals enrolled in the pilot study. Results reveal that patients undergoing PT via the telehealth service report a positive response to the service on a number of factors: (1) Flexibility/ease of set up of appointments; (2) Avoid expense and time related to travelling to appointments; (3) Allows support people to more easily attend the appointment; (4) Individuals can get home easily; (5) May spare unnecessary visits. Conclusions This pilot study reveals that providing PT via telehealth improves access to PT while maintaining quality of care and support.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".