Availability and Readability of Online Patient Education Materials Regarding Regional Anesthesia Techniques for Perioperative Pain Management
Bibliographic record
Abstract
OBJECTIVE: Patient education materials (PEM) should be written at a sixth-grade reading level or lower. We evaluated the availability and readability of online PEM related to regional anesthesia and compared the readability and content of online PEM produced by fellowship and nonfellowship institutions. METHODS: With IRB exemption, we constructed a cohort of online regional anesthesia PEM by searching Websites from North American academic medical centers supporting a regional anesthesiology and acute pain medicine fellowships and used a standardized Internet search engine protocol to identify additional nonfellowship Websites with regional anesthesia PEM based on relevant keywords. Readability metrics were calculated from PEM using the TextStat 0.1.4 textual analysis package for Python 2.7 and compared between institutions with and without a fellowship program. The presence of specific descriptive PEM elements related to regional anesthesia was also compared between groups. RESULTS: PEM from 17 fellowship and 15 nonfellowship institutions were included in analyses. The mean (SD) Flesch-Kincaid Grade Level for PEM from the fellowship group was 13.8 (2.9) vs 10.8 (2.0) for the nonfellowship group (p = 0.002). We observed no other differences in readability metrics between fellowship and nonfellowship institutions. Fellowship-based PEM less commonly included descriptions of the following risks: local anesthetic systemic toxicity (p = 0.033) and injury due to an insensate extremity (p = 0.003). CONCLUSIONS: Available online PEM related to regional anesthesia are well above the recommended reading level. Further, fellowship-based PEM posted are at a higher reading level than PEM posted by nonfellowship institutions and are more likely to omit certain risk descriptions.
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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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".