Transient vs Permanent Congenital Hypothyroidism in Ontario, Canada: Predictive Factors and Scoring System
Bibliographic record
Abstract
CONTEXT: The apparent increased incidence of congenital hypothyroidism (CH) is partly due to increased detection of transient disease. OBJECTIVE: This work aims to identify predictors of transient CH (T-CH) and establish a predictive tool for its earlier differentiation from permanent CH (P-CH). METHODS: A retrospective cohort study was conducted of patients diagnosed with CH from 2006 to 2015 through Newborn Screening Ontario (NSO). RESULTS: Of 469 cases, 360 (76.8%) were diagnosed with P-CH vs 109 (23.2%) with T-CH. Doses of levothyroxine predicting T-CH were less than 3.9 μg/kg at age 6 months, less than 3.0 μg/kg at ages 1 and 2 years, and less than 2.5 μg/kg at age 3 years. Descriptive statistics and multivariable logistic modeling demonstrated several diverging key measures between patients with T-CH vs P-CH, with optimal stratification at age 1 year. Thyroid imaging was the strongest predictor (P < .001). Excluding imaging, significant predictors in the first year of life included thyroxine dose/kg (P < .001-.002), increase in thyrotropin (TSH) above the reference interval during treatment (P = .002), screening TSH (P = .03), and a history of maternal thyroid disease (P = .02). Based on the 1-year model without imaging, a risk score was developed to identify children with T-CH who may benefit from an earlier trial off therapy, to reduce excess medicalization and health care costs. CONCLUSION: A levothyroxine dose of less than 3 μg/kg at ages 1 and 2 years and less than 2.5 μg/kg at age 3 years can be predictive of T-CH. A novel risk score was developed that can be clinically applied to predict the likelihood of a successful trial off therapy for a given patient at age 1 year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".