Readability of Healthcare Literature for Gastroparesis and Evaluation of Medical Terminology in Reading Difficulty
Bibliographic record
Abstract
BACKGROUND: Gastroparesis is a chronic condition that can be further enhanced with patient understanding. Patients' education resources on the Internet have become increasingly important in improving healthcare literacy. We evaluated the readability of online resources for gastroparesis and the influence by medical terminology. METHODS: Google searches were performed for "gastroparesis", "gastroparesis patient education material" and "gastroparesis patient information". Following, all medical terminology was determined if included on Taber's Medical Dictionary 22nd Edition. The medical terminology was replaced independently with "help" and "helping". Web resources were analyzed with the Readability Studio Professional Edition (Oleander Solutions, Vandalia, OH) using 10 different readability scales. RESULTS: The average of the 26 patient education resources was 12.7 ± 1.8 grade levels. The edited "help" group had 6.6 ± 1.0 and "helping" group had 10.4 ± 2.1 reading levels. In comparing the three groups, the "help" and "helping" groups had significantly lower readability levels (P < 0.001). The "help" group was significantly less than the "helping" group (P < 0.001). CONCLUSIONS: The web resources for gastroparesis were higher than the recommended reading level by the American Medical Association. Medical terminology was shown to be the cause for this elevated readability level with all, but four resources within the recommended grade levels following word replacement.
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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".