Pixelated vision: Validation of the complaint with no objective findings
Bibliographic record
Abstract
Background: We encountered a young female presenting with a complaint of “pixelated vision”. A brief literature search at the time of consultation showed no peer reviewed publications. Our objective was to use an infodemiologic approach to investigate the possible occurrence of an unidentified visual phenomenon.Methods: An Internet search with the metabrowser search engine Dogpile (www.dogpile.com) was conducted on April 24, 2018, using free text words “pixelated” and “vision.” The first 100 results were scanned for forum posts and cross-referenced to minimize duplication.Results: Of the first 100 results, 15 unique posts were identified. The majority of posts were made by the affected individual (n=14, 93%). Sex was female (n=5, 33%), male (n=2, 13%) or unknown (n=8, 53%). Onset was identified as new (n=10, 67%) or chronic (n=5, 33%).Conclusion: The availability and content of these forum postings suggest that pixelated vision is an uncommon, non-pathological visual phenomenon not yet documented in conventional medical literature.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".