The Case Correlation Assignment: Connecting Antemortem and Postmortem Data in the Senior Year at Iowa State University
Bibliographic record
Abstract
Instructors of the senior necropsy rotation at Iowa State University experienced difficulty ensuring similarity of case exposure and workload during the year. This was problematic during times of low caseload, as, without necropsy cases, there was no uniform method for training or assessing students. A new assignment, the Case Correlation Assignment (CCA), was created in order to improve the educational rigor and consistency of the rotation, increase utilization of necropsy cases as teaching material, and provide students more opportunities to correlate clinical pathology data with lesions. The CCA provides an opportunity for senior students to present and explain the antemortem and postmortem findings from an ISU-VTH patient in case report format. This illustrated report is submitted via WebCT. Students receive feedback on WebCT through a scoring rubric and written comments from the instructor. Since 2002, approximately 420 students have completed this assignment. The average score on the assignment over the four-year period is 94.7%. Despite complaints about the hard work required, students generally report that writing the CCA is a valuable learning experience. The CCA allows for greater utilization of necropsy cases and the incorporation of clinical pathology into necropsy cases. Currently, the CCA is used in a peer-assessment assignment in the junior pathology course and has been incorporated into case-based teaching courses. The CCA has been revised and expanded over the past four years in response to student feedback and to the discovery of new ways to utilize the completed assignments as teaching material.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".