Disease Knowledge and Attitudes toward Predictive Testing and Prenatal Diagnosis in Families with Machado-Joseph Disease from the Azores Islands (Portugal)
Bibliographic record
Abstract
OBJECTIVE: Machado-Joseph disease (MJD) reaches its highest prevalence world-wide in the Azores, thus constituting a public health problem in these islands. The aim of the study was thus to (1) determine the level of knowledge about the disease; (2) estimate the expected level of request for predictive testing, and (3) analyse the intentions of at-risk individuals concerning their reproductive decisions. METHODS: A questionnaire on these points was distributed to 42 affected and 36 at-risk individuals. RESULTS: As expected, the educational level of the respondents was significantly associated with the level of knowledge about the disease. The survey indicated that 83.3% of the at-risk individuals would make use of predictive test and that 77.8% would make use of prenatal diagnosis. Of the latter, 36.1% would terminate pregnancy if confronted with a positive result for the fetus. CONCLUSIONS: The level of knowledge about MJD in the Azorean families is considered to be fair. Although the actual behavior can prove to be different from the intentions put forward by at-risk individuals based solely on the results of this study we can estimate that the request for a predictive test would be quite high. The intentions expressed by at-risk individuals seem to indicate that the prenatal diagnosis will have an effect on their reproductive decisions. Results obtained certify the importance of implementing genetic testing for MJD in the Azores.
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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 | 0.000 | 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 | 0.001 | 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) | 0.002 | 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 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".