Data from: ChatGPT performance on radiation technologist and therapist entry to practice exams
Bibliographic record
Abstract
This dataset contains the data needed to reproduce all results and figures described in "ChatGPT performance on radiation technologist and therapist entry to practice exams". Details about the data collection can be found in the paper referenced below. Briefly, ChatGPT (GPT-4) was prompted with multiple choice questions from 4 practice exams provided by the Canadian Association of Medical Radiation Technologists (CAMRT). ChatGPT was promted with the questions from each exam 5 times between July 17 and August 13, 2023. Table 1, below, provides details about the dates for data collection. Variable descriptions question: Question number, provided by CAMRT. Skipped question numbers indicate image-based questions that were excluded from the study. discipline: Indicates the CAMRT exam discipline, abbreviated as follows RAD: radiological technology MRI: magnetic resonance NUC: nuclear medicine RTT: radiation therapy question_type: Indicates the type of competency being assessed by the question (Knowledge, Application, or Critical thinking). Competency categories were assigned by CAMRT. corrrect_response: The correct multiple choice response ("A", "B", "C", or "D"), assigned by CAMRT. attempt1-5: ChatGPT's response to the multiple choice questions for attempts 1 through 5, indicated using the letters "A", "B", "C", or "D". In a few cases, ChatGPT did not provide a reference to a multiple choice response and "NA" is recorded in the dataset. Note: The long-form questions from CAMRT and answers provided by ChatGPT are not available as a part of this dataset. Table 1: Dates for data collection Attempt 1 Attempt 2 Attempt 3 Attempt 4 Attempt 5 Radiological technology 2 Aug 2023 2 Aug 2023 8 Aug 2023 9 Aug 2023 11 Aug 2023 Magnetic resonance 17 Jul 2023 18 Jul 2023 18 Jul 2023 9 Aug 2023 12 Aug 2023 Nuclear medicine 8 Aug 2023 9 Aug 2023 12 Aug 2023 12 Aug 2023 12 Aug 2023 Radiation therapy 9 Aug 2023 12 Aug 2023 12 Aug 2023 13 Aug 2023 13 Aug 2023
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.056 |
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".