Protocol for a national intervention programme aimed to reduce unwarranted variation and overuse of shoulder arthroscopy in Norway
Bibliographic record
Abstract
Several advice-based approaches have aimed to reduce the overuse of procedures characterised as low-value healthcare services but have so far shown marginal impact on utilisation rates. Differences in utilisation rates tend to be associated with type of healthcare provider. Commercial providers have a tendency to have higher utilisations rates compared with non-profit providers. Norway has a publicly funded universal health coverage system where healthcare services are mainly provided by publicly owned hospitals in addition to a small number of non-profit hospitals and commercial hospitals reimbursed by the regional health authorities. According to previous experiences, unwarranted variation in utilisation rates can be associated with the type of healthcare provider. The aim of the present study is to evaluate the impact of tailoring the interventions on reducing overuse of low-value care based on an analysis of the clinical pathways and the various types of healthcare providers. The National Decision Forum in Norway has decided that acromion resection in patients with impingement symptoms and repair of non-traumatic cuff rupture should not be offered as standard treatment. The present protocol describes the planned intervention to reduce the overuse of these procedures.
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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 | 0.045 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.121 | 0.018 |
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".