Acute hot joints on the medical take: tapping into the skills of our workforce
Bibliographic record
Abstract
Acute oligoarthritis is a common presentation to secondary care, with septic arthritis the most serious cause.The inpatient mortality from septic arthritis is 7-15%, despite antibiotics use (1).Bacterial arthritis in England has an incidence of 1 in 49 000/100 000 person-years (2).Arthrocentesis, remains the gold-standard in differentiating septic from non-septic causes.Prompt diagnosis of acute oligoarthritis remains fundamental in avoiding unnecessary antibiotic and reducing hospital admission (3).Acute oligoarthritis is managed by medical teams in many NHS hospitals.Additionally, more common causes of acute hot joints, e.g., gout and pseudogout, often arise in elderly and multimorbid patients, in whom an important differential is septic arthritis, highlighting the need for urgent arthrocentesis.Arthrocentesis was incorporated within the compulsory curriculum for Core Medial Trainees (CMT) in 2009 in the United Kingdom (UK).Since implementation of Internal Medicine Training (IMT) and its new curriculum, this requirement has been removed for all IMT trainees.The rationale for this was delivering a more holistic training focused on "a small number of high-level learning outcomes rather than a large number of granular competencies" (3).Therefore, trainees often progress to IMT3 level and above without having performed or observed arthrocentesis.Yet, they are expected to run the acute unselected medical take with remote consultant supervision to meet the requirement at their Annual Review of Competency Progression (ARCP).Meanwhile, in comparison to the UK's IMT with its North American Internal Medicine (IM) counterparts, arthrocentesis remains a core competency for IM certification in the United States (US) and Canada (5).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".