Bibliographic record
Abstract
OBJECTIVES: Annually, millions of pairs of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) tests are ordered. These enzymes are highly correlated, and ALT is far more specific diagnostically than AST. To reduce AST testing, we suggest measuring AST only when ALT exceeds a predetermined limit. METHODS: We derived the proportions of elevated ASTs that would not be measured based on 15 months of paired inpatient and outpatient ALT and AST data. RESULTS: For inpatients, a 35 U/L ALT limit for initiating AST testing would reduce AST testing by 51%, missing only 3% and 7.5% of ASTs exceeding 50 U/L and 35 U/L, respectively. In outpatients, AST testing can be reduced by more than 65%, with fewer missed elevated ASTs (0.5% and 2% of the ASTs exceeding 50 U/L and 35 U/L, respectively). CONCLUSIONS: Conservatively, $100 million could be saved annually in the US health care budget by selectively limiting AST testing in just the US outpatient environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".