The Population Based Incidence of Myelodysplastic Syndrome (MDS): Utility of Multiple Data Sources and Follow-up.
Bibliographic record
Abstract
Abstract Abstract 245 Background: The incidence of MDS in Canada is not known. Diagnosis of MDS is often challenging as dysplastic features on bone marrow may be non-specific, requiring exclusion of other disorders. The province of Manitoba, with a population of 1.2 million, has a cancer registry which has included patients with MDS since 2001. In this province, hematology diagnostic services are centralized at two teaching hospitals and the few bone marrows performed outside are reviewed centrally. This provided us with the opportunity to use registry data and bone marrow records to determine the incidence of MDS. We hypothesized that for an accurate estimate, a proportion of MDS cases would require follow-up data. Methods: Retrospective study to examine all cases of MDS, which included chronic myelomonocytic leukemia (CMML) diagnosed in Manitoba. All adult Manitobans diagnosed with MDS and CMML (excluding RAEB-T), from 01/2006 to 12/2007 were identified from the cancer registry using ICD-O-3 topography code C42.1 and morphology codes 9980/3, 9982/3, 9983/3, 9985/3, 9986/3, 9987/3, 9989/3 and 9945/3. Bone marrow records for the same period were reviewed to identify all cases that had features of MDS. The clinical charts of all these patients were reviewed centrally to exclude those whose clinical course or repeat investigations suggested an alternative diagnosis. Results: A total of 80 patients with newly diagnosed MDS were identified. The age adjusted incidence of MDS was 3.26/100,000. Incidence was higher in men (4.05/100,000) as against women (2.57/100,000). Incidence varied significantly with age at diagnosis: <49yr: 0.12; 50–59yr: 2.24; 60–69yr: 10.63; 70–79yr: 20.41 and >80yr: 21.93. Eleven cases (13.75%) were not known to the cancer registry but were detected on reviewing the bone marrow data. From the registry, nine cases (11.25%) were excluded as the chart review and follow-up revealed alternative diagnoses. Conclusions: The incidence of MDS for Manitoba is similar to published rates in Europe and the USA. This may be an underestimation of the actual incidence, as elderly patients may not undergo bone marrow examination if the therapeutic intervention is only supportive. Cancer registries that include MDS based on one-time bone marrow reports should include a review process of confirming or excluding the diagnosis of MDS based on follow up investigations and course of illness. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".