Finalist Resident Teams Announced for MindGames
Bibliographic record
Abstract
Back to table of contents Previous article Next article APA & MeetingsFull AccessFinalist Resident Teams Announced for MindGamesSearch for more papers by this authorPublished Online:13 Apr 2020https://doi.org/10.1176/appi.pn.2020.4b21AbstractThe three institutions whose teams of residents were the finalists in this year’s MindGames competiton—the quiz show-like contest pitting residents in a test of psychiatric knowledge—are SUNY Downstate Health Science University, Brooklyn; New York Presbyterian/Weill Cornell Medicine; and the University of Arizona, Tucson.The final competition, which has become a popular attraction at the Annual Meeting, will not occur this year. Due to public health concerns around the COVID-19 pandemic, the meeting cannot be held.These are the triumphant residents for the three institutions:SUNY Downstate Health Science University at Brooklyn: Rishab Gupta, M.D., Rebecca Parra, M.D., and Amvrine Ganguly, M.D.New York Presbyterian/Weill Cornell Medicine: Charles Shaffer, M.D., Leonid Kapulsky, M.D., and Joseph Stujenske, M.D., Ph.D.University of Arizona, Tucson: Michelle Singh, M.D., Philip Lam, D.O., and Jonathan Lavi, M.D.MindGames is open to all psychiatry residency programs in the United States and Canada. The preliminary competition is in February, when teams of three residents take a 60-minute online test consisting of 150 multiple-choice questions. The questions follow the ABPN Part I content outline, covering both psychiatry and neurology, with a few difficult history-of-psychiatry questions. The winners, announced earlier this year at the annual meeting of the American Association of Directors of Psychiatric Residency Training in Dallas, are the three top-scoring teams with the fastest posted times. ■ ISSUES NewArchived
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.704 | 0.542 |
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".