Haemophilia in a real‐world setting: the value of clinical experience in data collection
Bibliographic record
Abstract
At the 8th Annual Congress of the European Association for Haemophilia and Allied Disorders (EAHAD) held in Helsinki, Finland, in February 2015, Pfizer sponsored a satellite symposium entitled: 'Haemophilia in a real-world setting: The value of clinical experience in data collection' Co-chaired by Riitta Lassila (Helsinki University Central Hospital, Helsinki, Finland) and Gerry Dolan (Guy's and St Thomas' Hospital, London, UK); the symposium provided an opportunity to explore the practical value of real-world data in informing clinical decision-making. Gerry Dolan provided an introduction to the symposium by describing what is meant by real-world data (RWD), stressing the role RWD can play in optimising patient outcomes in haemophilia and highlighting the responsibility of all stakeholders to collaborate in continuous data collection. Kristian Juusola (Oulu University Hospital, Oulu, Finland) then provided personal experience as a haemophilia nurse around patient views on adherence to treatment regimes, and how collecting insights into real-world use of treatment can shape approaches to improving adherence. The importance of elucidating pharmacokinetic parameters in a real-world setting was then explored by Vuokko Jokela (Helsinki University, Helsinki, Finland). Finally, Alfonso Iorio (McMaster University, Hamilton, Ontario, Canada) highlighted the importance of quality data collection in translating clinical reality into scientific advances.
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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 | 0.626 | 0.704 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".