What Information Are Patients Receiving from the Internet about the Operative and Nonoperative Management of Acute Appendicitis?
Bibliographic record
Abstract
INTRODUCTION: Recent studies suggest that nonoperative management of appendicitis (NOMA) may be a reasonable option for managing uncomplicated acute appendicitis. We examined the Internet to see if patients are likely to find the information they need to make an informed decision between the 2 options. METHODS: A list of 29 search terms was established by a focus group and then entered into Google, resulting in 49 unique webpages, each reviewed by 3 reviewers. Consensus was obtained for bias (surgery, NOMA, or balanced), webpage type, JAMA score, reading grade, and DISCERN score, a measure of quality of written information for patients. RESULTS: Thirty of the 49 websites (61%) favored surgery, while 13 (27%) favored NOMA, and 6 sites (12%) provided balanced information. Twelve of 49 sites (24%) did not list NOMA as an option. The majority of patient-directed (11/12 = 92%) and physician-directed (7/9 = 78%) webpages favored surgery, whereas academic webpages presented a more balanced distribution. Academic and physician-directed webpages ranked higher than commercial and news webpages (median ranks 3 and 4 vs. 7.5 and 8). Only 8/49 sites (16%) mentioned that the presence of a fecalith predicts the failure of NOMA. Reading grades were almost all well above the recommended grade 8 level. CONCLUSION: Most of the webpages available on the Internet do not provide enough information, nor are they sufficiently understandable to allow most patients to make an informed decision about the current options for the management of acute appendicitis.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".