Bibliographic record
Abstract
BACKGROUND: Asplenic patients in general have poor knowledge about their condition. Patients are increasingly turning to the Internet for their health care information, therefore this is a resource that many asplenic patients will use. The aim of our study was to determine the quality of information on the Internet for asplenic patients. METHODS: We identified websites by entering "splenectomy OR spleen removal" into 3 Internet search engines on July 28, 2008. The top 50 English-language websites from each search engine were included in our analysis. We evaluated the websites with our own 21-point content scale as well as 4 commonly used quality-assessment tools. All websites were analyzed independently by 2 reviewers. Correlations were made between the quality assessment instruments, content, readability and target audience. RESULTS: We included 89 websites in the study. The mean content score percentage for all websites was 49% (95% confidence interval 44%-54%). The long-term risk of infection was mentioned in 84% of websites, and the need for vaccination was mentioned in 79%. The mean quality assessment tool score was 61%, and the mean reading grade level was 11. CONCLUSION: Whereas websites on average did not cover most of the information that asplenic patients should receive, the long-term risk of serious infection and the need for vaccination was consistently mentioned. Websites were inconsistent with respect to adhering to standards advocated by the quality assessment instruments we used, and the mean reading grade level was far above what is recommended for patient literature.
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 | 0.009 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".