The pros and cons of the fourth revision of thalassaemia screening programme in Iran
Bibliographic record
Abstract
Objective To evaluate the repercussions of recent changes to the cut-offs used in the first screening step of the pre-marital screening programme for thalassaemia prevention in Iran. Methods The profiles of 984 subjects referred to a genetic laboratory, and the tests of 242 parents of children with thalassaemia major were assessed for red blood cell (RBC) indices, haemoglobin (Hb) A2 levels and results of Hb electrophoresis. Results Of 407 suspected thalassaemia minor (STM) cases, 18 proved positive for thalassaemia minor on molecular analysis (18/407, confidence interval 2.6-6.9%). If the revised screening cut-offs had been used to determine who would undergo molecular analysis, two of these cases would not have been identified. Only 4.4% of suspected cases with lower than normal RBC indices (mean corpuscular volume <80 fl and mean corpuscular Hb <27 pg) and HbA2 (<3.5%) were diagnosed with thalassaemia minor. Conclusion The thalassaemia major prevention programme is performed in two separate steps. One step involves the screening of subjects and identification of β-thalassaemia minor, suspected cases for thalassaemia minor (STM), and normal subject groups. The other step concerns the identification of thalassaemia minor in the STM group. Changing the cut-offs at the first screening step does not result in significant improvement from an economic view, and is associated with significant risk at the second screening step.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".