An internet-based educational algorithm for the work-up, diagnosis and management of patients with myelodysplastic syndromes from the Canadian Consortium on MDS
Bibliographic record
Abstract
Background Myelodysplastic Syndrome (MDS) treatments reduce transfusion dependence, delay progression to acute leukemia, and may improve survival. The Canadian Consortium on MDS (CCMDS) developed the MDS ClearPath, a comprehensive tool for the diagnosis, work-up and management of MDS of any risk category at any point during a patient’s disease course. Methods The draft ClearPath algorithm was revised by 60 Canadian hematologists, finalized by consensus of the Steering Committee and went live in 2013. The update went online in January 2023. Results An approach to the diagnosis and management of MDS is provided. Appropriate investigations are detailed, current scoring systems are included as is a prognostic calculator, and an IPSS-M calculator link is included. Treatments (erythropoiesis-stimulating agents; lenalidomide; hypomethylating agents; immunosuppressive therapy; supportive care [transfusions; antibiotics; bleeding prevention; iron chelation]; investigational agents; links to clinical trial websites) are detailed, including dosing/administration; monitoring; dose adjustments; expected response; side effect management; and provincial reimbursement. Added were details on luspatercept, decitabine and decitabine/cedazuridine; recommendations for mutation analysis; WHO and ICC 2022 classifications; the IPSS-M and Clinical Frailty scores; familial predisposition testing; and response assessment criteria. Recommendations are made where data are lacking. The Treatment Wizard, a series of questions specific to clinical status, leads to treatment recommendations; the self-directed mode is the overall algorithm. References with abstract links are included, and information panels included throughout. The ClearPath in English or French is available at www.MDSClearPath.org; a (free) iPad app is being updated. Discussion The CCMDS presents an internet/app-based algorithm to support MDS management, with recommendations designed to assist in the standardization of MDS care.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.015 |
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".