Patient Questions Related to Peritoneal Dialysis: An Analysis of Online Search Data
Bibliographic record
Abstract
Background: End-stage renal disease patients must often decide whether to receive peritoneal dialysis (PD) or hemodialysis (HD), and much of the academic literature has focused on comparing quality of life differences between PD and HD. However, the type of information that patients themselves seek when considering PD is relatively unknown. As more patients utilize the Internet to access health information, understanding PD search trends can help identify areas for targeted improvement in patient education. Here, we address this knowledge gap by characterizing online search data related to PD. Methods: In May 2023, Google search data based on the term “Peritoneal Dialysis” 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 “Peritoneal Dialysis” 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 1,747 PAA questions for “Peritoneal Dialysis.” Coding of the top 100 questions revealed that the greatest number of questions related to Procedure (41) (e.g., “What are the steps in peritoneal dialysis?”), Complications (14) (e.g., “What are the side effects of peritoneal dialysis?”), Definition (10) (e.g., “What are the types of peritoneal dialysis?”), Prognosis (8) (e.g., “How long can you live on peritoneal dialysis?”), Quality of Life (8) (e.g., “Can you swim with a peritoneal dialysis catheter?”), and then Comparison to Other Forms of Dialysis (6) (e.g., “Which is better hemodialysis or peritoneal dialysis?”). Thirteen questions were uncategorized (e.g., “How long is training for peritoneal dialysis?”). Conclusions: The most common theme for questions related to PD was Procedure, which reveals a knowledge gap in the procedural aspects of PD. While it is important to compare different treatment options from a quality of life standpoint, providers should take steps to thoroughly educate patients about the procedural details surrounding PD (e.g., the equipment used or the steps involved in PD) to address patients’ most common questions and informational needs.
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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 | 0.017 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.042 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".