Your results may vary: the imprecision of medical measurements
Bibliographic record
Abstract
### What you need to know Clinicians and patients need to interpret a multitude of medical measurements. These are often central to monitoring health and informed decision making. Has the serum cholesterol concentration come down since starting a statin? Have vitamin D levels gone up? Is the dose of thyroid medication correct? An understanding of the imprecision of medical measurements is essential to answer any of these questions. Even when laboratory and industry scientists have optimised their diagnostic testing processes to minimise inaccuracies, there always remains an error in any clinical measurement due to unavoidable, naturally occurring variability. This practice pointer explains the nature of measurement errors and offers a practical guide to both estimating the confidence interval of a single result and deciding if changes between serial laboratory tests reflect true changes or simply fluctuations based on analytical or biological variation. ### How this article was made This article was based on a review of the available biological variation data for select routine clinical chemistry measurements as collated by the European Federation of Clinical Chemistry and Laboratory Medicine (https://biologicalvariation.eu/) and in select cases identified by …
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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 | 0.094 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".