Virtual sorting hat <sup>™</sup> technology for the matching of candidates to residency training programs
Bibliographic record
Abstract
Background The matching of medical students and trainees to appropriate training programmes poses many challenges, including financial cost and applicant stress. There are few studies that have examined alternatives to the current process of matching candidates to specialist training. Case reports from Hogwarts School of Witchcraft and Wizardry ™ have suggested that wearable technology may be used to assign individuals with particular sets of skills and virtues to an appropriate house. Methods Investigators developed a modified sorting hat in the form of an online, cross‐sectional survey. The virtual sorting hat was delivered to medical students at the National University of Ireland, Galway, and medical practitioners practising in the associated hospitals and communities. Pearson's chi‐square was used to demonstrate correlations between the allocation of participants to Hogwarts’ houses by virtual sorting hat technology and expressed higher specialist training preference. Results Virtual sorting hat technology, applied to medical undergraduates and postgraduates, allocated most participants to Hufflepuff ™ (44%) and Ravenclaw ™ (32%). Allocation to Gryffindor was associated with preference for surgery and allocation to Slytherin ™ with preference for psychiatry. Conclusion Virtual sorting hat technology requires significant refinement before application to medical muggles ™ .
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".