Characteristics of Decedents in Medicare Advantage and Traditional Medicare
Bibliographic record
Abstract
Methods | Waiver for this study was obtained through Alberta Health Services for collection of aggregate data.Based on these various sources of recommendations, a guideline was formed (Box) and was approved by Alberta Health Services.This guideline led to a separate form being developed and issued by laboratory services.Physicians now had to identify the indication for testing vitamin D levels.Indications other than those identified in the guideline did not allow for testing of vitamin D levels.This new requisition was implemented on April 1, 2015.Before this date, for several years, the number of annual 25-hydroxy vitamin D assays was approximately 310 000 per year (ie, 1 in 14 Albertans).We measured the monthly number of vitamin D order requests before and after introducing a new procedure for ordering tests.Results | From April 1 to December 31, 2015, subsequent to the creation of a new ordering form, 20 609 vitamin D tests were ordered.During this 9-month period, one would have expected, based on historical data, that 256 027 tests would have been ordered.This intervention thus led to a 92.0%reduction in the number of vitamin D tests ordered, a savings of about $4 million USD per year ($3 million in the period studied thus far) (Figure).Although criteria-based interventions are susceptible to methods that manage to avoid adhering to the requirements, a rebound in test ordering has not been observed 9 months after implementation.Our laboratory ordering system is both paper-based and electronic; thus, this type of intervention can be used in many different systems.Discussion | Criteria-based approaches to the implementation of the Choosing Wisely recommendations have been examined in practice for other tests, including the commonly ordered tests for rheumatoid factor antibody and anti-nuclear antibodies, as well as advanced imaging (computed tomographic scan, bone scan, or magnetic resonance imaging) for spinal pain.5,6 Using a criteria-based approach to test ordering not only reduces the number of tests that would be ordered but it does so without missing clinically relevant conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".