Evaluation of thyroid test utilization through analysis of population-level data
Bibliographic record
Abstract
BACKGROUND: Inappropriate laboratory test utilization can result in unnecessary patient testing and increased healthcare costs. While several thyroid function tests are available, thyroid-stimulating hormone (TSH) is recommended as the first-line test for investigating and monitoring thyroid dysfunction. We evaluate thyroid test utilization in Northern Alberta in terms of testing patterns, frequencies, and reflex cutpoints. METHODS: This retrospective study analyzed thyroid test requests from January to December 2014. Each request was designated as appropriate or potentially inappropriate as per clinical practice guidelines and Choosing Wisely recommendations, and the frequencies of each testing pattern were calculated. Sub-analysis was performed to categorize testing patterns based on physician specialty. The number of test requests per patient was determined to assess the appropriateness of testing frequency. Receiver operating characteristic (ROC) curves were generated to define optimal TSH cutpoints for automatic reflex to FT4 testing. RESULTS: Of 752,217 test requests, approximately 10% were potentially inappropriate in terms of testing patterns. Free thyroxine (FT4) and free triiodothyronine (FT3) requested with TSH accounted for 59% of all potentially inappropriate test requests, and 49% of requests from endocrinologists (ENDO) were potentially inappropriate, occurring most frequently among those with less experience. Excessive testing frequencies were observed in 869 patients, accounting for 9382 test requests. Adjustment of our TSH reflex cutpoint would significantly increase specificity for identifying a low FT4 without compromising sensitivity. CONCLUSIONS: This study suggests that questionable testing patterns, excessive testing frequencies, and suboptimal reflexive testing cutpoints contribute to inappropriate thyroid test utilization.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".