Law of the Vital Few: Choosing Variables of Disease Activity in Rheumatoid Arthritis
Bibliographic record
Abstract
The Italian economist Vilfredo Pareto noticed that 20% of the pea pods in his garden contained 80% of the peas. His “Law of the Vital Few”1 seems to describe a wide variety of situations where 80% of the observed effect can be explained by 20% of the causes. The treatment of rheumatoid arthritis (RA) was revolutionized in the 1980s and 1990s by a small number of simple but powerful advances. Robust trials demonstrated the value of cheap, readily available drugs such as methotrexate. Simple clinical outcome measures such as the Disease Activity Score (DAS) were validated. Because our goals have since evolved to focus on smaller numbers of patients with resistant disease or poor prognosis or to personalize therapy, more complex and expensive techniques and interventions seem to be required to achieve better outcomes. The longterm goal of therapy in RA is the preservation of function and prevention of joint damage. Treat-to-target trials such as TICORA demonstrate that both doctors and patients underestimate how much therapy is needed to achieve this2. Not surprisingly, perhaps, more therapy leads to less inflammation and better short-term and longterm outcomes. Current “best practice” in early RA is based on the use of composite scores such as the DAS or its 28-joint version (DAS28) to direct escalation of therapy … Address correspondence to Dr. E.M. Vital, Chapel Allerton Hospital, Leeds LS7 4SA, UK; E-mail: e.m.j.vital{at}leeds.ac.uk
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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 | 0.030 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| 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".