G01 Huntington disease predictive testing protocol: a 5 year review of practice
Bibliographic record
Abstract
Background The protocol for HD predictive testing at our Centre includes a psychosocial assessment as step 2 of a 3-step process. In future, this resource may not be available. Aims To review our experience with our HD predictive testing protocol. Methods We performed a 5-year review of patients referred for pre-symptomatic testing for HD and solicited feedback from 10 recent patients. Results/outcome A total of 104 individuals at 50% risk requested predictive testing for HD. The majority (87; 84%) completed the protocol and received results. Almost all agreed to meet a neuropsychiatrist and none were flagged as poor candidates for predictive testing. Of the 17 that did not complete the protocol, 13 (76.5%) discontinued after the first session with the genetic counsellor, 2 never returned for results, 1 stopped after blood test, and 1 saw neuropsychiatrist but never had blood drawn. Conversations with the 10 most recent patients revealed that most felt the process worked well. Wait-time for results was the only complaint. Opinions differed regarding the helpfulness of neuropsychiatric consultation and few patients opted to access neuropsychiatry after the protocol was complete. Conclusions Assessing readiness is essential to HD predictive testing, as is psychological support after testing. Feedback from our patients was mixed with regard to usefulness of meeting with neuropsychiatrist. The significant number of patients (12.5%) who did not continue with the protocol after first session with the genetic counsellor vs the small number (3%) who did so after the neuropsychiatric evaluation, suggests the majority of patients are making decisions very early on in the protocol.
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 | 0.172 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".