[Practice variation in diagnostic testing for dementia; a nation-wide overview].
Bibliographic record
Abstract
OBJECTIVE: To determine variation in diagnostic strategies for diagnosing dementia between Dutch hospitals. DESIGN: Descriptive, retrospective research based on claim data of Dutch health insurers. METHOD: Information on the use of diagnostic ancillary services carried out from 2015 to 2018 was collected via national-level insurance claims for patients who received a (new) diagnose-coding for dementia in 2018. Hospitals were included in the analysis if they diagnosed >50 patients with dementia. We distinguished academic medical centres (AMC), non-academic training hospitals (TH) and general hospitals (GH). RESULTS: In 2018, 20.073 new cases of dementia were diagnosed in 71 hospitals. The percentages of patients undergoing MRI/CT-imaging ranged from 37 to 99% (median 76.7%), neuropsychological-assessment from 0-89% (median 31.8%), cerebrospinal fluid examination from 0-14% (median 2.4%), PET/SPECT-imaging from 0-16% (median 6.2%) and electroencephalography from 1-20% (median 5.8%). Practice variation was comparable in AMCs, THs and GHs and was evidently skewed for PET/SPECT-imaging, electroencephalography and cerebrospinal fluid examination. There were no distinct differences according to case-mix characteristics or hospital volume. The percentage of patients subjected to ancillary diagnostic investigations decreased sharply with increasing age. CONCLUSION: In the Netherlands, diagnostic ancillary methods used vary widely between hospitals both in frequency and modality. This variation may be driven by limited evidence of diagnostic accuracy and added value of different diagnostic tests, variations in doctor and patient preferences and differences in available diagnostic techniques per hospital. Further exploration of this heterogeneity may help to identify a strategy that combines the most benefit with the least burden.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".