Bibliographic record
Abstract
It is widely claimed that can't manage what you don't measure. Likely this quote is derived from statements made by Lord Kelvin to the Institution of Civil Engineers. In his 1883 address, he claimed: ...when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind... (1) A number of tools have been developed to accurately assess our hemophilia patients' progress and response to changing treatments. These include radiographic scores (like the Pettersson (X-ray) score and the compatible magnetic resonance imaging score), musculoskeletal assessment tools (like the Hemophilia Joint Health Scale), functional assessment questionnaires (like the Haemo-philia Activities List) or observational tools (like the Functional Independence Scale for Haemophilia), as well as summary measures of health - which are often called quality of life (QoL) or health-related quality of life (HRQL) tools(2-15). These tools have given clinicians and researchers powerful new ways to describe their patients' illnesses, but there are a number of considerations that should be addressed in future work. This commentary will address some of these issues.
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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 | 0.048 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.019 | 0.033 |
| Insufficient payload (model declined to judge) | 0.002 | 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".