Increased prevalence of iron‐overload associated endocrinopathy in thalassaemia <i>versus</i> sickle‐cell disease
Bibliographic record
Abstract
Iron-overload associated endocrinopathy is the most frequently reported complication of chronic transfusion therapy in patients with thalassaemia (Thal). This study compared iron-overloaded subjects with Thal (n = 142; 54%M; age 25.8 +/- 8.1 years) and transfused sickle-cell disease (Tx-SCD; n = 199; 43%M, 24.9 +/- 13.2 years) to non-transfused SCD subjects (non-Tx-SCD; n = 64, 50%M, 25.3 +/- 11.3 years), to explore whether the underlying haemoglobinopathy influences the development of endocrinopathy. Subjects were recruited from 31 centres in the USA, Canada and the UK. Subjects with Thal had more evidence of diabetes (13% vs. 2%, P < 0.001), hypogonadism (40% vs. 4%, P < 0.001), hypothyroidism (10% vs. 2%, P = <0.001) and growth failure (33% vs. 7%, P < 0.001), versus Tx-SCD. Fifty-six per cent of Thal had more than one endocrinopathy compared with only 13% of Tx-SCD (P < 0.001). In contrast, Tx-SCD was not different from non-Tx-SCD. Multivariate analysis indicated that endocrinopathy was more likely in Thal than SCD [Odds Ratio (OR) = 9.4, P < 0.001], with duration of chronic transfusion a significant predictor (OR = 1.4 per 10 years of transfusion, P = 0.04). Despite iron overload, endocrinopathy was not increased in Tx-SCD versus non-Tx-SCD, suggesting that the underlying disease may modulate iron-related endocrine injury. However, because transfusion duration remained a significant predictor of endocrinopathy, these data should be confirmed in SCD subjects that have been chronically transfused for longer periods of time.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".