Internet-Based Resources Frequently Provide Inaccurate and Out-of-Date Recommendations on Preoperative Fasting
Bibliographic record
Abstract
Preoperative fasting is important to avoid morbidity and surgery delays, yet recommendations available on the Internet may be inaccurate. Our objectives were to describe the characteristics and recommendations of Internet resources on preoperative fasting and assess the quality and readability of these websites. We searched the Internet for common search terms on preoperative fasting using Google® search engines from 4 English-speaking countries (Canada, the United States, Australia, and the United Kingdom). We screened the first 30 websites from each search and extracted data from unique websites that provided recommendations on preoperative fasting. Website quality was assessed using validated tools (JAMA Benchmark criteria, DISCERN score, and Health on the Net Foundation code [HONcode] certification). Readability was scored using the Flesch Reading Ease score and Flesch-Kincaid Grade Level. A total of 87 websites were included in the analysis. A total of 48 websites (55%) provided at least 1 recommendation that contradicted established guidelines. Websites from health care institutions were most likely to make inaccurate recommendations (61%). Only 17% of websites encouraged preoperative hydration. Quality and readability were poor, with a median JAMA Benchmark score of 1 (interquartile range 0-3), mean DISCERN score 39.8 (SD 12.5), mean reading ease score 49 (SD 15), and mean grade level of 10.6 (SD 2.7). HONcode certification was infrequent (10%). Anesthesia society websites and scientific articles had higher DISCERN scores but worse readability compared with websites from health care institutions. Online fasting recommendations are frequently inconsistent with current guidelines, particularly among health care institution websites. The poor quality and readability of Internet resources on preoperative fasting may confuse patients.
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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 | 0.006 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".