Patient perspectives of a diagnosis of myeloproliferative neoplasm in a case control study
Bibliographic record
Abstract
BACKGROUND: Myeloproliferative neoplasms (MPNs) including the classic entities; polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis are rare diseases with unknown aetiology. The MOSAICC study, is an exploratory case-control study in which information was collected through telephone questionnaires and medical records. METHODS: As part of the study, 106 patients with MPN were asked about their perceived diagnosis and replies correlated with their haematologist's diagnosis. For the first time, a patient perspective on their MPN diagnosis and classification was obtained. Logistic regression analyses were utilised to evaluate the role of variables in whether or not a patient reported their diagnosis during interview with co-adjustment for these variables. Chi square tests were used to investigate the association between MPN subtype and patient reported categorisation of MPN. RESULTS: Overall, 77.4 % of patients reported a diagnosis of MPN. Of those, 39.6 % recognised MPN as a 'blood condition', 23.6 % recognised MPN as a 'cancer' and 13.2 % acknowledged MPN as an 'other medical condition'. There was minimal overlap between the categories. Patients with PV were more likely than those with ET to report their disease as a 'blood condition'. ET patients were significantly more likely than PV patients not to report their condition at all. Patients from a single centre were more likely to report their diagnosis as MPN while age, educational status, and WHO re-classification had no effect. CONCLUSIONS: The discrepancy between concepts of MPN in patients could result from differing patient interest in their condition, varying information conveyed by treating hematologists, concealment due to denial or financial concerns. Explanations for the differences in patient perception of the nature of their disease, requires further, larger scale investigation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".