<p>Googling on Colonoscopy: A Retrospective Analysis of Search Engine Statistics</p>
Bibliographic record
Abstract
PURPOSE: Colonoscopy is a gold standard for screening and diagnosis of colorectal cancer (CRC). The data from the search engine may reveal what information on coloscopy gains the attention of Internet users. We aimed to investigate Google searches trends and terms related to colonoscopy. PATIENTS AND METHODS: We retrieved statistics searches related to colonoscopy using Google Trends (GT) and Google Ads (GA) for the period from April 2016 to March 2020. The GT data was used for the analysis of time and regional search patterns worldwide. GA data for Australia, Canada, Ireland, New Zealand (NZ), Poland, the United Kingdom (UK), and the United States (US) were used to calculate the search volume of categories of queries related to colonoscopy. RESULTS: Globally, the relative search volume on colonoscopy has increased until the COVID-19 outbreak and revealed seasonal variation: the highest interest was observed in March (CRC awareness month), and the lowest during December (Christmas holidays). The highest number of searches per 1000 Google users-years was done in Poland (59.62) and the lowest in the UK (19.46). Most commonly, Google users searched for details on colonoscopy techniques (Australia, Canada, Ireland, NZ), anesthesia during the procedure (Poland), facility performing colonoscopy (UK, US). In all seven countries, less than 2% of queries concerned with bowel preparation before the procedure. CONCLUSION: Before the COVID-19 pandemic, the interest in colonoscopy has increased among Google users. Google users may underestimate the importance of proper bowel preparation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".