Investigating Online Search Trends to Improve Patient Education in Nephrolithiasis
Bibliographic record
Abstract
Background: Nephrolithiasis can cause severe pain and affects one in ten individuals globally. Successful treatment requires patient cooperation, and managing the condition can be complex. While a variety of interventions ranging from citrate supplements to hydrochlorothiazide to shock wave lithotripsy exist, patients’ informational needs for nephrolithiasis are not well understood within the literature. As an increasing number of patients turn to the Internet for health information, analyzing search trends offers an opportunity to identify knowledge gaps and improve health literacy. Here, we characterize online search data related to nephrolithiasis to inform efforts to improve targeted patient education. Methods: In May 2023, Google search data based on the term “Kidney Stones” were analyzed using “Search Response” (https://searchresponse.io/), a search engine optimization tool. Searches were performed for the most common People Also Ask (PAA) questions against a dataset of over 150 million queries, and the top 100 PAA questions relevant to the “Kidney Stones” keyword were ranked based on popularity. Two reviewers (AZ and MT) independently grouped the questions into categories adapted from standards in the literature, and a third reviewer (KMS) resolved any discrepancies. Results: The Search Response tool generated 19,376 PAA questions for “Kidney Stones.” Coding of the top 100 questions revealed that the greatest number of questions related to Management and Nutrition (31) (e.g., “What is the best food to eat when you have kidney stones?”), Treatment and Medication (25) (e.g., “What is the best treatment for kidney stones?”), Definition, Diagnosis, and Symptoms (20) (e.g., “What are the 4 types of kidney stones?”), and then Causes, Risk Factors, and Prevention (15) (e.g., “What are the main causes of kidney stones?”). Nine questions were uncategorized (e.g., “How painful is a stent for kidney stones?”). Conclusions: The most common themes for questions regarding nephrolithiasis were Management and Nutrition, followed by Treatment and Medication; Definition, Diagnosis, and Symptoms; and Causes, Risk Factors, and Prevention. Therefore, targeted provider information about nutrition and other lifestyle interventions should be prioritized to address patients’ most common concerns regarding nephrolithiasis, and may help improve health literacy for this widespread condition.
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 | 0.030 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.037 | 0.045 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".