Frequency that Laboratory Tests Influence Medical Decisions
Bibliographic record
Abstract
BACKGROUND: Among the variables that influence medical decisions, laboratory tests are considered to be among the most important and frequently used. The influence of laboratory tests on medical decisions has been difficult to estimate. The goal of this study was to estimate the number of patient encounters that included a laboratory test. METHODS: We extracted information for 72196 patient encounters from 1-week intervals each quarter of a year from our comprehensive academic medical center electronic medical record. The patients examined represent a comprehensive range of clinical conditions and medical services. We determined for which encounters laboratory and other orders existed. RESULTS: Overall 35% of encounters had 1 or more laboratory tests ordered. However, the percent varied markedly with patient care areas. For inpatient, emergency department, and outpatient populations, 98%, 56%, and 29%, respectively, had 1 or more laboratory tests ordered. CONCLUSIONS: Our observations support that it is not possible to use a single number to categorize the frequency with which laboratory tests occur in patient encounters. Utilization of laboratory tests varied with type of medical service with almost all inpatients, approximately half of emergency department patients, and nearly one-third of outpatients having laboratory tests during their healthcare visit.
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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 | 0.006 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".