Does conventional early life academic excellence predict later life scientific discovery? An assessment of the lives of great medical innovators
Bibliographic record
Abstract
BACKGROUND: Perhaps, as never before, we need innovators. With our growing population numbers, and with increasing pressures on our education systems, are we in danger of becoming more rigid and formulaic and increasingly inhibiting innovation? When young can we predict who will become the great innovators? For example, in medicine, who will change clinical practice? AIMS: We therefore determined to assess whether the current academic excellence approach to medical school entrance would have captured previous great innovators in medicine, assuming that they should all have well fulfilled current entrance requirements. METHODS: The authors assembled a list of 100 great medical innovators which was then approved, rejected or added to by a jury of 12 MD fellows of the Royal Society of Canada. Two reviewers, who had taken both the past and present Medical College Admission Test as part of North American medical school entrance requirements, independently assessed each innovator's early life educational history in order to predict the innovator's likely success at medical school entry, assuming excellence in all entrance requirements. RESULTS: Thirty-one percent of the great medical innovators possessed no medical degree and 24% would likely be denied entry to medical school by today's standards (e.g. had a history of poor performance, failure, dropout or expulsion) with only 24% being guaranteed entry. Even if excellence in only one topic was required, the figure would only rise to 41% certain of medical school entry. CONCLUSION: These data show that today's medical school entry standards would have barred many great innovators and raise questions about whether we are losing medical innovators as a consequence. Our findings have important implications for promoting flexibility and innovation for medical education, and for promoting an environment for innovation in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".