Recommendations for authors of manuscripts reporting inhibitor cases developed in previously treated patients with hemophilia: communication from the SSC of the ISTH
Bibliographic record
Abstract
Aim The scope of this recommendation is to provide guidance for reporting of inhibitor cases in previously treated patients (PTPs) with hemophilia A. This guidance is intended to improve transparency and completeness of reporting of observed events; it does not cover planning, executing or analyzing original studies aimed at the assessment of inhibitor rates. Recommendation We recommend that for each case of inhibitor development reported in a published paper, a paragraph or a table is included in the main publication reporting as a minimum the underlined data fields in Table . We recommend transparent reporting when any of the suggested information is not available. We recommend that particular care is used in reporting the timeline of events by clearly identifying a reference time-point. We suggest that journals in the field adopt this guidance as instructions for the authors and as a guide for reviewers. Conclusion Development of inhibitors in PTPs is a very rare event. Standardized reporting of inhibitor characteristics will contribute to generating a body of evidence otherwise not available. Case by case reporting of the recommended data elements may shed light on the natural history and risk factors of inhibitor development in PTPs and be useful for tailoring care in similar future cases.
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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 | 0.243 | 0.783 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.035 | 0.024 |
| Insufficient payload (model declined to judge) | 0.071 | 0.173 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".