Orthopaedic Web Links (OWL): A Way to Find Professional Orthopaedic Information on the Internet
Bibliographic record
Abstract
BACKGROUND: Finding useful high-grade professional orthopaedic information on the Internet is often difficult. Orthopaedic Web Links (OWL) is a searchable database of vetted online orthopaedic resources. OWL uses a subject directory (OWL Directory) and a custom search engine (OWL Web) to provide a list of resources. The most effective way to find readily accessible, full text on-subject material suitable for education of an orthopaedic surgeon or trainee has not been defined. QUESTIONS/PURPOSES: We therefore (1) proposed a method for selecting topics and evaluating searches and (2) compared the search results from an orthopaedic-specific directory (OWL Directory), a custom search engine (OWL Web), and standard Google searches. METHODS: A scoring system for evaluation of the search results was developed for standardized comparison. Single words and sets of three words from randomly selected examination questions provided the search strings to compare the three strategies. RESULTS: For single keyword searches, the OWL Directory scored highest (16.4/50) of the three methods. For the three keywords searches, OWL Web had the highest mean score (26.0/50), followed by Google (22.8/50), and the OWL Directory (1.0/50). OWL Web searches had higher scores than Google searches, while returning 800 times fewer search results. CONCLUSION: The OWL Directory of orthopaedic subjects on the Internet provides a simple browsable category structure to find information. The OWL Web search engine scored higher than Google and resulted in a greater proportion of valid, on-subject, and accessible resources in the search results.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".