Assessing volume and variation of low-value care practices in the Netherlands
Bibliographic record
Abstract
BACKGROUND: There have been contributions to quantify the volume of low-value care practices in the USA, Canada and Australia but we have no knowledge about the volume in Europe. The purpose of this study was to assess the volume and variation of Dutch low-value care practices. METHODS: We conducted a cross-sectional study with data of a Dutch healthcare insurance company from general practioners (GP's) and hospitals in the Netherlands from 2016. We used all billing claims made by healthcare providers of 3.5 million Dutch inhabitants. We studied Choosing Wisely recommendations in order to select low-value care practices. We used the percentage low-value care practices per hospital and number of low-value care practices per GP as outcomes. RESULTS: We assessed the volume of low-back imaging by GPs, screening of patients over 75 years for colorectal cancer and diagnosing varices with Doppler or Plethysmography. We found that 0.4% (range 0-7%) of the eligible patients received low-value screening for colorectal cancer and 8.0% (range 0-88%) of eligible patients received low-value diagnosing of varices. About 52.4% of the GPs ordered X-rays and 11.2% ordered magnetic resonance imagings of the lumbosacral spine. Most healthcare providers did not provide the measured low-value care practices. However, 1 in 12 GPs ordered at least one low-back X-ray a week. CONCLUSIONS: The three Choosing Wisely recommendations showed a lot of practice variation; many healthcare providers did not order these low-value diagnostic tests; a minor part did order a substantial amount, low-back spine radiology in particular. These healthcare providers should start reducing these activities.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".