Levothyroxine prescribing and laboratory test use after a minor change in reference range for thyroid-stimulating hormone
Bibliographic record
Abstract
BACKGROUND: Prescribing of levothyroxine and rates of thyroid function testing may be sensitive to minor changes in the upper limit of the reference range for thyroid-stimulating hormone (TSH) that increase the proportion of abnormal results. We evaluated the population-level change in levothyroxine prescribing and TSH testing after a minor planned decrease in the upper limit of the reference range for TSH in a large urban centre with a single medical laboratory. METHODS: Using provincial administrative data, we compared predicted volumes of TSH tests with actual TSH test volumes before and after a planned change in the TSH reference range. We also determined the number of new levothyroxine prescriptions for previously untreated patients and the rate of changes to the prescribed dose for those on previously stable, long-term levothyroxine therapy before and after the change in the TSH reference range. RESULTS: Before the change in the TSH reference range, actual and predicted monthly volumes of TSH testing followed an identical course. After the change, actual test volumes exceeded predicted test volumes by 7.3% (95% confidence interval [CI] 5.3%–9.3%) or about 3000 to 5000 extra tests per month. The proportion of patients with newly “abnormal” TSH results almost tripled, from 3.3% (95% CI 3.2%–3.4%) to 9.1% (95% CI 9.0%–9.2%). The rate of new levothyroxine prescriptions increased from 3.24 (95% CI 3.15–3.33) per 1000 population in 2013 to 4.06 (95% CI 3.96–4.15) per 1000 population in 2014. Among patients with preexisting stable levothyroxine therapy, there was a significant increase in the number of dose escalations (p < 0.001) and a total increase of 500 new prescriptions per month. INTERPRETATION: Our findings suggest that clinicians may have responded to mildly elevated TSH results with new or increased levothyroxine prescriptions and more TSH testing. Knowledge translation efforts may be useful to accompany minor changes in reference ranges.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".