Patient 3: 19-year-old man with acute knee pain and swelling and a 2-year history of recurrent similar symptoms
Bibliographic record
Abstract
See page 950 for the question. ### Diagnosis Patellar dislocation. The plain radiographs demonstrate a small fragment of bone adjacent to the lateral femoral condyle on both the skyline and the anterior–posterior (AP) radiographs (fig 3, showing the fragment on the skyline view). On the skyline view there is also an osteochondral defect near the junction of the patellar apex and the lateral facet (arrow). The MRI images (fig 4a) show marrow oedema in the medial and inferior patella and in the lateral femoral condyle. There is oedema in the soft tissues adjacent to the medial patellar retinaculum, and the medial patellar retinaculum is wavy and less well visualised than normal (fig 4b). Findings are consistent with recent patellar dislocation. Figure 3 Skyline view of the patella. Arrow demonstrates osteochondral fragment (same as fig 1B with arrow). Figure 4 (A,B) MRI scan of the patient’s knee (axial proton density with fat saturation). (A) Same as fig 2a with arrows; (B) same as fig 2b with arrows. The patient was started on a rehabilitation programme. Acute patellar dislocation is a common injury, especially in the second to third decades of life.1 Patients who have an episode of patellar dislocation are often not conscious of the transient dislocation, making clinical diagnosis difficult. …
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".