Keeping guidelines current: A six-month evaluation of Wiki-based lung cancer guidelines.
Bibliographic record
Abstract
e17587 Background: The challenge for clinical practice guidelines is keeping them regularly updated with new evidence, and widely disseminated in a cost effective manner. This is difficult with printed guidelines and so Cancer Council Australia developed an internet based wiki platform for guidelines and assessed the impact of wiki lung cancer treatment guidelines 6 months after their development. Methods: The key steps in guideline development of identifying questions, literature searching, appraisal of papers, experts writing evidence-based recommendations and wide public consultation and dissemination were integrated in an access-protected wiki. Google web analytics was used to monitor usage. New papers are added and the writers continuously engaged in updating their questions. The evaluation spanned 6 months from May to November 2012. Results: The lung cancer guidelines had 67 clinical questions and 2076 articles were critically appraised. In the first month during public consultation there were 1055 visits to the site and 2955 visits in the following 5 months. The guidelines were accessed from a mobile device by 7% for consultation and 11% subsequently. During the consultation 80% of visits were from Australia, 6% New Zealand, 4% USA and 2% UK. In the consultation period 56% visits resulted from organic search traffic, 31% direct access and 13% referrals and subsequently 72% from searches, 19% direct and 11% referrals. The guideline partner Cancer Australia was the major source of referral (26% during consultation and 25% subsequently) as well as Cancer Council’s main website 9% during the consultation and 38% subsequently. Facebook was responsible for 6-8% of referrals highlighting the role of social media in promoting guidelines. The average highest visit duration by a country was 9.06 minutes in the consultation month (Australia) and 7.24 for Canada. Bounce rates vary widely from 20% (Germany) to above 70% (USA, UK, India). Conclusions: Both clinicians and the public will engage with wiki guidelines and they reach a wide international audience.
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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 | 0.110 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".