Bibliographic record
Abstract
A 48-year-old man was seen in the emergency department with a massively swollen knee and no history of trauma. On physical examination, the knee appeared greatly distended and tense. The patient could not fully flex it and found weight-bearing difficult. The knee was only minimally warm to the touch and the patient had no fever. On examination of other joints, there were no abnormalities. The patient had no comorbid conditions, but he did mention previous episodes of less-severe swelling of the same knee over the previous 5 years, occurring 5–12 months apart and resolving spontaneously over several weeks. Needle aspiration yielded a large amount of frank blood, with immediate relief of symptoms. The residual swelling resolved over the following few weeks. Subsequently, the patient underwent magnetic resonance imaging of the knee (Fig. 1) and arthrography (Fig. 2) for further evaluation. FIG. 1. Figure 1. Continued. Figure 1. Continued. FIG. 2. Figure 2. Continued. In view of the clinical history and imaging findings, what diagnoses would you consider? For the diagnosis and a discussion of the findings see page 226.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".