Rule-based automatic criteria detection for assessing quality of online health information
Bibliographic record
Abstract
Automatically assessing the quality of health related Web pages is an emerging method for assisting consumers in evaluating online health information. We propose a rule-based method of detecting technical criteria for automatic quality assessment in this paper. Firstly, we defined corresponding measurable indicators for each criterion with the indicator value and expected location. Then candidate lines that may contain indicators are extracted by matching the indicator value with the content of a Web page. The actual location of a candidate line is detected by analyzing the Web page DOM tree. The expression pattern of each candidate line is identified by regular expressions. Each candidate line is classified into a criterion according to rules for matching location and expression patterns. The occurrences of criteria on a Web page are summarized based on the results of line classification. The performance of this rule-based criteria detection method is tested on two data sets. It is also compared with a direct criteria detection method. The results show that the overall accuracy of the rule-based method is higher than that of the direct detection method. Some criteria, such as authors name, authors credential and authors affiliation, which were difficult to detect using the direct detection method, can be effectively detected based on location and expression patterns. The approach of rule-based detecting criteria for assessing the quality of health Web pages is effective. Automatic detection of technical criteria is complementary to the evaluation of content quality, and it can contribute in assessing the comprehensive quality of health related Web sites.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".