Comparative analysis of Shwachman-Diamond syndrome to other inherited bone marrow failure syndromes and genotype-phenotype correlation
Bibliographic record
Abstract
Hashmi SK, Allen C, Klaassen R, Fernandez CV, Yanofsky R, Shereck E, Champagne J, Silva M, Lipton JH, Brossard J, Samson Y, Abish S, Steele M, Ali K, Dower N, Athale U, Jardine L, Hand JP, Beyene J, Dror Y. Comparative analysis of Shwachman-Diamond syndrome to other inherited bone marrow failure syndromes and genotype–phenotype correlation. Our knowledge of the phenotypes of inherited bone marrow failure syndromes (IBMFSs) derives from case reports or case series in which only one IBMFS was studied. However, the substantial phenotypic overlap necessitates comparative analysis between the IBMFSs. Shwachman–Diamond syndrome (SDS) is an IBMFS that the appreciation of what comprises its clinical phenotype is still evolving. In this analysis we used data on 125 patients from the Canadian Inherited Marrow Failure Study (CIMFS), which is a prospective multicenter population-based study. Thirty-four cases of SDS patients were analyzed and compared to other patients with the four most common IBMFSs on the CIMFS: Diamond Blackfan anemia, Fanconi anemia (FA), Kostmann/severe congenital neutropenia and dyskeratosis congenita (DC). The diagnosis of SDS, FA and DC was often delayed relative to symptoms onset; indicating a major need for improving tools to establish a rapid diagnosis. We identified multiple phenotypic differences between SDS and other IBMFSs, including several novel differences. SBDS biallelic mutations were less frequent than in previous reports (81%). Importantly, compared to patients with biallelic mutations, patients with wild type SBDS had more severe hematological disease but milder pancreatic disease. In conclusion, comprehensive study of the IBMFSs can provide useful comparative data between the disorders. SBDS-negative SDS patients may have more severe hematological failure and milder pancreatic disease.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".