An evaluation of the adequacy of outpatient monitoring of thyroid replacement therapy
Bibliographic record
Abstract
OBJECTIVES: Hypothyroid patients managed with excessive or insufficient thyroid replacement therapy are often difficult to clinically recognize. Monitoring may prevent or minimize the consequences of adverse drug events (ADEs). We sought to develop an explicit model of medication monitoring and to evaluate monitoring processes and ADEs in patients taking levothyroxine. METHODS: A retrospective chart review of 400 outpatients receiving levothyroxine therapy between 1 January 2000 and 1 January 2001 at a large North American tertiary care hospital. We measured the proportion of patients satisfying minimum monitoring criteria, experiencing specific monitoring errors and having levothyroxine-related ADEs. Explicit monitoring criteria were derived from the literature and through expert opinion. Adverse drug events were identified using structured implicit reviews. RESULTS: Overall, only 56% (95% confidence interval [95% CI] 51-62%) of the patients prescribed levothyroxine received the minimal recommended monitoring. Errors were identified at all stages of the monitoring model. Patients who received the recommended monitoring had fewer levothyroxine-related ADEs (1% vs. 6%, P=0.013) than those who did not. Minority status (white people 2% vs. black people 4% vs. Hispanics 14%, P=0.023) and primary language (English 3% vs. Non-English 20%, P=0.002) were the patient characteristics associated with levothyroxine-related ADEs. CONCLUSION: Only half of outpatients taking levothyroxine at one tertiary care hospital received the recommended monitoring during one year of follow-up. Levothyroxine-related ADEs were more frequent in patients with lower-quality monitoring and in minorities and non-English speakers.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".