The digital epidemiology of dysencephalia splanchnocystica, AKA meckel–gruber syndrome: Retrospective analysis and geographic mapping via google trends
Bibliographic record
Abstract
Background: Genetic diseases are diverse and many of which have debilitating consequences affecting the individual, the society, and the economy. Trends databases, including Google Trends database, can be used to estimate the digital epidemiology of these diseases. Digital Epidemiology is valuable when it comes to conditions of low prevalence as in the case of ciliopathies including that of Meckel–Gruber Syndrome.Aims and Objectives: To assess the digital epidemiology and the geographic mapping of Meckel- Gruber syndrome via a trends database of the surface web.Materials and Methods: Google Trends database will be usedfor geographic mapping and retrospective analysis of interest of users of the Surface Web. The aim is to infer and predict the digital epidemiology of Meckel–Gruber Syndrome. A retrospective analysis is conducted as far as the trends database permits (2004-2017). The trends database was explored using the thematic expression of keywords specific to Meckel–Gruber Syndrome including its synonyms. Subsequently, descriptive and inferential statistics were carried out to estimate the digital epidemiology as well as the geographic mapping. The aim was to conclude the existence of any significant change in web users’ interest and the variation of that interest versus geography (country) and chronology (time).Results: Concerning geographic mapping, signals of web users were found to be originating from the United States (68.49%) and Finland (31.51%). Globally, the average value of the relative interest of surface web users in Meckel–Gruber Syndrome was 34.10 (+/- 14.59). There was an overall decline in web users’ attention towards the condition for the period 2004−2010 versus 2011−2017 (20.06 vs 4.88, p-value<0.001) and for period 2004-2006 versus 2007-2009 (29.14 vs 14.19, p=0.001).Conclusion: Digital epidemiological analysis has been proven feasible with good accuracy via Googles Trends. In the case of Dysencephalia Splanchnocystica, the geographic mapping of the surface web has been limited to the developed world. Prospectively, Google Trends can be integrated into a predictive early warning system to anticipate any change in the interest of the users of the Indexed Web in a particular disease including genetic ones.Asian Journal of Medical Sciences Vol.9(5) 2018 81-86
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".