Supplementary Material for: Cross-Talk between Available Guidelines for the Management of Patients with Beta-Thalassemia Major
Bibliographic record
Abstract
Efforts to optimize the management of patients with β-thalassemia major (TM) continue to expand. Evidence from biomedical research evaluating safe and careful processing measures of blood products, the efficacy and safety of oral iron chelators, and noninvasive techniques for the assessment of iron overload are translated into better patient outcomes. The construction of TM management guidelines facilitated the incorporation of such evidence into practice. However, as several aspects of the management of TM remain controversial or governed by resource availability, a concern regarding potential variations in recommendations made by the different guidelines becomes rational, especially for physicians treating TM patients outside countries where the guidelines were constructed. In this work, we overview currently available guidelines for the management of TM and explore apparent similarities and differences between them. The evaluated guidelines included the Thalassaemia International Federation, US, Canadian, UK, Italian and Australian guidelines. We noted a general consensus for most aspects of management, although some guidelines provided more comprehensive and contemporary recommendations than others. We did not identify differences warranting concern, although minor differences in iron overload assessment strategy and more notable variations in the recommendations for iron chelation therapy were observed.
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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 | 0.002 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.296 | 0.050 |
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".