The Top 25 Laboratory Tests by Volume and Revenue in Five Different Countries
Bibliographic record
Abstract
Objectives: To compare the most common diagnostic/laboratory tests across five different referral hospitals by volume and revenue. Methods: The authors obtained data on volumes and reimbursement rates for the most common 25 tests at the five hospitals with which they are affiliated and organized them to be as comparable as possible. Simple descriptive statistics were used to make cross-country comparisons. Results: There are strong similarities across all five hospitals in the top five tests by both volume and revenue. However, the top five by volume differ from the top five by revenue. Reimbursement rates also follow common patterns, being lowest for the most common biochemical test; intermediate for the most common hematology and microbiology tests, respectively; and highest for the most common pathology test. Conclusions: Most of the most common tests also appear in the new Essential Diagnostics List. This may inform plans for universal health coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".