Headache: an important symptom possibly linked to white matter lesions in thalassaemia
Bibliographic record
Abstract
Neurological manifestations are reported only occasionally in patients with thalassaemia and are given much less prominence than the complications related to anaemia and iron overload. White matter changes (WMCs) on magnetic resonance imaging (MRI) in patients with thalassaemia were first reported two decades ago but the significance of these lesions remains unclear. We studied the neurological and cognitive manifestations in 82 older patients with thalssaemia [25 Thalassaemia major (TM), 24 thalassaemia intermedia (TI) and 33 haemaglobin E β thalassaemia (EBT)] and 80 controls, and found that headaches were more common in thalassaemia patients (50/82, 61%) than in controls (18/80, 22·5%: P < 0·001). WMCs on MRI were found in 20/82 (24·3%) patients and 2/29 (6·9%) controls had (P = 0·078). WMC were more common among those with headaches (17/50: 34%) than in those without headache (3/32; 9·3%) (P = 0·023). WMCs were not associated with reduction of cognition. Nevertheless, cognition was lower in the TI and EBT groups compared with those with TM (P = 0·002). The association of headache with WMC in thalassaemia has not been reported before and warrants further study.
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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 | 0.001 |
| 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 | 0.000 | 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".