The Challenges of Measuring Adherence to Clinical Treatment Recommendations in Spondyloarthritis
Bibliographic record
Abstract
Clinical treatment recommendations are intended to provide evidence-based guidance to healthcare practitioners about appropriate care for specific clinical situations, with the goal of using that guidance to improve patient care. Unfortunately, translating treatment recommendations from the library literature review to routine clinical care can present a significant challenge. Many groups may propose treatment recommendations for the same disorder, and in some cases these recommendations may be contradictory. Further, the number of treatment recommendations that emerge at a steady pace make it difficult for the clinician to keep up to date. In 2017 so far, the American College of Rheumatology has already published 2, and the European League Against Rheumatism has published 6 sets of treatment recommendations. Despite the frequency of recommendations, it is difficult to assess whether they are being applied routinely to clinical care. In this issue of The Journal , Harvard, et al 1 address this problem by evaluating a system to define adherence to anti-tumor necrosis factor (TNF) use recommendations in spondyloarthritis (SpA). Additionally, they evaluate how adherence to anti-TNF use recommendations in SpA affects economic and health outcomes, while controlling for adherence to other SpA recommendations. Harvard, et al ’s study included 469 patients who met the Assessment of Spondyloarthritis international Society (ASAS) criteria for SpA2 in … Address correspondence to Dr. S. Rohekar. E-mail: sherry.rohekar{at}sjhc.london.on.ca
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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 | 0.402 | 0.699 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".