Googling for Neurological Disorders: From Seeking Health-Related Information to Patient Empowerment, Advocacy, and Open, Public Self-Disclosure in the Neurology 2.0 Era
Bibliographic record
Abstract
Since its introduction, the internet has played a major role in reshaping patient-physician communication and interactions, having fostered a shift from a paternalistic to a patient-centered model. Because of its dynamic nature, the internet has been used as a platform to not only disseminate knowledge-favored by improved access to an increasing wealth of available resources-but also to spread advocacy and awareness, contribute to fund-raising, and facilitate open, public self-disclosure of one's own disease, thus eliminating any taboo and reducing the stigma associated with it. The era of Medicine 2.0 is characterized by openness, collaboration, participation, and social networking. The current situation is completely different from the time when Lorenzo Odone's parents, after his diagnosis of adrenoleukodystrophy, decided to attend medical school in order to collect information about a devastating, unknown disease and had to contend with medical authorities at that establishment to convince them of the alleged effectiveness and safety of their discovered therapeutics. Orphan and rare neurological diseases have currently received recognition on web-based resources. However, while the intention is not to ridicule Odone's family legacy and the "complicated lessons" they have reported, some issues should be carefully addressed by health authorities, such as the reputability, reliability, and accuracy of material available on the internet and prevention of the dissemination of material that could instill illusions and unjustified hopes in individuals seeking medical treatment. Neurologists should be aware of such digital resources, participate in web-based activities, and recommend select high-quality websites to their patients.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".