Screening and diagnostic clinical algorithm for paroxysmal nocturnal hemoglobinuria: Expert consensus
Bibliographic record
Abstract
OBJECTIVE: Paroxysmal nocturnal hemoglobinuria (PNH) is a severe, life-threatening disorder for which early diagnosis is essential. However, given the rarity of the disease and non-specificity of symptoms, correct diagnosis may be delayed or missed. While various hematologic guidelines note common signs and symptoms associated with PNH, international expert consensus based on real-world clinical experience and an actionable algorithm for non-specialists to facilitate screening and diagnosis are lacking. The objective of the study is to develop a clinically relevant, consensus-driven screening and diagnostic algorithm on PNH for non-specialist clinicians. METHODS: An expert advisory committee of PNH experts from North America, Europe, and Japan was convened, and a modified Delphi methodology was employed to develop an algorithm to assist non-specialist clinicians in identifying signs/symptoms of PNH and conducting appropriate differential diagnosis. Twelve globally representative Delphi panelists with clinical expertise in PNH were identified and recruited. Panelists provided their differential diagnosis for 5 blinded case studies via 2 rounds of online questionnaires. Responses mentioned by >50% of panelists in the first round were included in the second-round questionnaire, at which point consensus was attained if >80% of panelists agreed on an approach. RESULTS: Consensus was reached for 95% of screening and diagnostic decision points and 90% of tests required at decision points. CONCLUSION: These results facilitated development of a consensus-based, clinically relevant algorithm, providing non-specialist clinicians with actionable guidance on PNH screening and diagnosis.
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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 | 0.063 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".