Uncertainties in Big Data When Using Internet Surveillance Tools and Social Media for Determining Patterns in Disease Incidence-Reply.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

2 citing papers in PubMed.

1. Google Searches and Detection of Conjunctivitis Epidemics Worldwide.Ophthalmology · 2019
   Article
2. Clinical Age-Specific Seasonal Conjunctivitis Patterns and Their Online Detection in Twitter, Blog, Forum, and Comment Social Media Posts.Investigative ophthalmology & visual science · 2018
   Article

Corrections and comments

• Comment on
  Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns.2016
• Comment on
  Uncertainties in Big Data When Using Internet Surveillance Tools and Social Media for Determining Patterns in Disease Incidence.2017

Authors and funding

3 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

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