Medical genetics and patient use of the Internet
Bibliographic record
Abstract
Clinical experience suggests that the Internet is increasingly becoming a resource for patients seen in medical genetics. A prospective analysis was performed exploring patient use of the Internet prior to attending a medical genetics appointment. We administered 200 questionnaires assessing: 1) the frequency of patient use of the Internet for genetic information, 2) factors associated with Internet use, 3) patient assessment of the value of the information, and 4) patient views of the responsibility of medical genetics professionals to be familiar with Internet information. Results show that 77% (153/200) of patients have access to the Internet of which 29% (44/153) report searching the Internet for genetic information. A correlation was found between patient use of the Internet and reason for referral (p<0.001), presence of a specific diagnosis (p<0.001), and frequency of Internet use (p<0.05). Overall, 80% (33/41) of patients found Internet information useful. Seventy-four percent (115/155) believed that medical genetics professionals have a responsibility to review relevant Internet sites for accuracy and 80% (123/153) felt that professionals should provide their patients with appropriate and useful Internet sites. These results suggest that the role of medical genetics professionals is changing as a result of the development of the Internet.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".