Bibliographic record
Abstract
To the Editor: Many believe that physicians are perfect, all-knowing beings. Despite having heard about clinical errors through news media, I also subscribed to this belief, and, consequently, carried a philosophy of perfection into my first clinical observership as a medical student. The physician I shadowed, a family doctor operating in a rural community, occasionally asked me about the patients’ cases, and I felt accomplished when responding correctly to the questions she fired. Then, she threw a curve ball. The next patient was a child who cut his leg, and the physician enthusiastically prompted me to suture the wound. For some, suturing a one-inch cut might be banal. For me, it was daunting. In all my three weeks of medical school, I had not yet learned about suturing. I was clueless, yet refusing seemed unviable. The family physician continued pointing the needle driver toward me, repeating that it was an easy procedure, something that I should know how to do. As I reached for the needle driver, I looked to the boy, finally noticing his terrified expression. I was so preoccupied with my own thoughts that I had neglected the patient. He deserved my honesty. He deserved quality care. My throat unfroze, and the words finally flowed: “I don’t feel comfortable doing the sutures. I’ve never done it before … but I would appreciate if I could watch how you would do this.” I waited for the physician’s reply. How incompetent are you? Are you really a medical student? You should know this already! You should consider another profession! I braced myself for the harsh criticism, but it never came. Instead, the physician apologized for assuming that I was comfortable with treating the patient and started explaining what to do. This was my first instance (of many) of admitting my inability to perform a task. Despite the family physician’s understanding words, I felt frustrated and embarrassed. However, it was these emotions that ultimately prompted me to improve my knowledge. Later that week, I began watching YouTube video tutorials on suturing techniques, and I registered for an upcoming surgical skills workshop. I believe that physicians have an innate desire to seek perfection, but no one is born knowing how to perform a cardiac exam or how take a social history. We all need first experiences to learn and grow, and admitting our limitations is essential for discovering our starting points. Jeffrey Lam Shin CheungFirst-year medical student, University of Toronto Medical School, Toronto, Ontario, Canada; [email protected]
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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 | 0.012 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.022 | 0.040 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".