Quality Assessment of Spinal Cord Injury Patient Education Resources
Bibliographic record
Abstract
STUDY DESIGN: Analysis of spinal cord injury patient education resources. OBJECTIVE: To assess the quality of online patient education materials written about spinal cord injury. SUMMARY OF BACKGROUND DATA: The use of online materials by health care consumers to access medical information presents unique challenges. Most Americans have access to the Internet and frequently turn to it as a first-line resource. METHODS: The quality of online patient education materials was evaluated via a readability analysis. Materials provided by the National Institute of Neurological Disorders and Stroke; Centers for Disease Control and Prevention; American Association of Neurological Surgeons; The National Spinal Cord Injury Association; Mayo Clinic; U.S. Department of Veterans Affairs; Kessler Institute for Rehabilitation; American Academy of Neurology; Paralyzed Veterans of America; and the Shepherd Center were assessed using the Flesch Reading Ease and Flesch-Kincaid Grade Level evaluations with Microsoft Office Word software. Unnecessary formatting was removed and the readability was evaluated with the spelling and grammar function. RESULTS: A total of 104 sections from 10 different Web sites were analyzed. Overall, the average values of the Flesch-Kincaid Grade Level (11.9) and Flesch Reading Ease (40.2) indicated that most Americans would not be able to fully comprehend this material. CONCLUSION: Results indicate that the language used on materials provided by the aforementioned sites is perhaps too advanced for the average American to fully comprehend. The quality of these education resources may be improved via Web site revisions, which might be beneficial for improved patient utilization. LEVEL OF EVIDENCE: 2.
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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".