The digital epidemiology of phenylketonuria, aka folling’s disease: retrospective analysis and geographic mapping via google trends
Bibliographic record
Abstract
Background: Phenylketonuria, commonly known as PKU, is an inherited disorder in which there is an abnormally elevated blood level of the amino acid phenylalanine leading to several pathologies affecting multiple organs including the central nervous system and resulting in debilitating intellectual disability and other neuropsychiatric disorders. Phenylalanine is a building block of several critical proteins within the biological systems.Aims and Objective: To assess the digital epidemiology and geographic mapping of Phenylketonuria.Materials and Methods: This study is a retrospective analytic (2013‑2017) of a very large database existing on the surface web known as Google Trends. it aims to extrapolate a statistical inference concerning the digital epidemiology and the geographic mapping of phenylketonuria. The trends database will be explored via thematic keywords specific to the condition of phenylketonuria including “Phenylketonuria [PKU]”, “Phenylalanine”, “Inborn errors of metabolism”, “Tetrahydrobiopterin”, and “Chromosome 12 (human)”.Results: The digital epidemiology is densely clustered in countries from the developed world, eastern Europe, and Latin America. Surface web users from China appears to possess the highest interest in phenylketonuria. The contribution of the Middle Eastern and Arabic countries to the geographic mapping did not exceed 10.51% at its best. Significant changes existed for year-to-year variations of trends. Statistical outliers were also found, the strongest of which was observed during April 2016 for which there’s no plausible explanation.Conclusion: Trends databases operating on the surface web represent potent tools of big data that can be exploited to assess the digital epidemiology and geographic mapping of countless phenomenon including rare genetic diseases and inborn errors of metabolism. There are also enormous potentials for real-time and predictive analytics of these databases when investing the application of automation in data collection and principles of machine learning.Asian Journal of Medical Sciences Vol.9(6) 2018 93-99
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".