Development of a Cadaveric Model for Arthrocentesis
Bibliographic record
Abstract
This article reports the development of a novel cadaveric model for future use in teaching arthrocentesis. In the clinical setting, animal safety is essential and practice is thus limited. Objectives of the study were to develop and compare a model to an unmodified cadaver by injecting one of two types of fluids to increase yield. The two fluids injected, mineral oil (MO) and hypertonic saline (HS), were compared to determine any difference on yield. Lastly, aspiration immediately after (T1) or three hours after (T2) injection were compared to determine any effect on diagnostic yield. Joints used included the stifle, elbow, and carpus in eight medium dog cadavers. Arthrocentesis was performed before injection (control) and yield measured. Test joints were injected with MO or HS and yield measured after range of motion (T1) and three hours post injection to simulate lab preparation (T2). Both models had statistically significantly higher yield compared with the unmodified cadaver in all joints at T1 and T2 (p<.05) with the exception of HST2 carpus. T2 aspiration had a statistically significant lower yield when compared to T1HS carpus, T1HS elbow, and T1MO carpus. Overall, irrespective of fluid volume or type, percent yield was lower in T2 compared to T1. No statistically significant difference was seen between HS and MO in most joints with the exception of MOT1 stifle and HST2 elbow. Within the time frame assessed, both models were acceptable. However, HS arthrocentesis models proved appropriate for student trial due to the difficult aspirations with MO.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".