Screening and diagnostic clinical algorithm for paroxysmal nocturnal hemoglobinuria: Expert consensus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it