Infodemiology of cataract in India: An analysis of online search behaviour using google trends from 2011 to 2022
Bibliographic record
Abstract
Background: Cataract is the leading cause of blindness, but vision can be restored with cataract surgery. Information-seeking behaviour on the internet influences the acceptance of cataract surgery. This study aimed to examine public online searches for cataract in India from January 2011 to December 2022. Methodology: The study retrieved online search data for ‘cataract’ and ‘cataract surgery’ from the Google Trends (GT) Explorer page. These searches were measured in terms of relative search volumes (RSVs). The researchers conducted a one-way analysis of variance to examine variations in quarterly ‘cataract’ searches across each year from 2011 to 2022. In addition, a joinpoint regression analysis was employed to identify significant changes in searching patterns over time with statistical significance. Results: The highest annual mean RSV for cataract was recorded in 2022 (88.58%) and 2021 (80.83%), and the lowest annual mean RSV was recorded in 2014 (53.50%). The joinpoint regression shows an average annual increase of 3.3% in cataract searches from 2011 to 2022. A statistically significant linear trend was observed from quarter 1 (April–June) to quarter 4 (January–March) in the year 2016–2017 (contrast estimates [CE] = 6.18, ηp2 = 64.4%), 2017–2018 (CE = 8.72, ηp2 = 77.5%), 2018–2019 (CE = 7.91, ηp2 = 65.8%) and 2020–2021 (CE = 21.45, ηp2 = 80.6%). Conclusion: There are a growing number of individuals searching for online health information related to cataracts, and medical professionals need to educate them on the proper selection of websites based on established guidelines and quality standards. The study indicates that there is a preference during winter months for seeking information about cataracts. Public health initiatives can collaborate with internet-based media platforms during this time frame to enhance public awareness, knowledge and access to surgical services for all eligible individuals.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".