Bibliographic record
Abstract
Any human who has ever experienced an acute gout flare understands how painful and debilitating this condition is. Unfortunately, due to the episodic nature of these acute flares that occur randomly due to transient fluctuations in urate levels, patients often underreport these attacks. This turns into a tragedy—patients are left untreated and develop chronic gouty arthritis where pain-free periods are infrequent and joint damage is abundant. The holy grail in clinical trials, whether acute or chronic gout, has been to understand the various nuances of adherence because we know that it leads to prevention and perhaps remission: how long can a patient stay flare free, how do we quantify it, and what measures should we use? Despite the increase in the number of gout cases,1 the disease is equally mismanaged in primary care and the rheumatology subspecialty. This is evident in the literature, with suboptimal dosing of urate-lowering drugs, intolerance to therapy, or poor patient compliance. Guidelines across the globe have highlighted the gaps in care and the poor quality of life. Gout is considered the most treatable arthritis in the Western World, with the pathophysiology directly related to uric acid metabolism and effective medications available to treat both acute episodes … Address correspondence to Dr. P. Khanna, Division of Rheumatology, Department of Internal Medicine, University of Michigan & AAVAMC, 300 North Ingalls, Ste 7C27, Ann Arbor, MI 48109-5422, USA. Email: pkhanna{at}umich.edu.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".