Bibliographic record
Abstract
To the Editor: Being a learner in medicine can feel like being in a constant—and sometimes disillusioning—state of catch-up. My relationship with learning in medical school was fragile. I questioned the superficial mnemonics, the esoteric medical trivia, the fascination with minutiae—medallions of competence that amplified knowledge differentials between neophytes and experts. There was too much fragmented information, often received all at once and sometimes lacking in clinical relevance. This knowledge felt like a privileged exorbitant excess that I struggled to master even as I finished medical school. Then, suddenly, we all became learners during the COVID-19 pandemic. When everyone knows very little about a disease, there is a peculiar equalization between the novice trainee and the expert clinician. Sheltered at home, I observed how medical journals and discussions became unusually lively and diverse—laden with uncertainty, debate, and enthusiasm. Even as a student, I could participate in a collective understanding of an invisible illness. There was a palpable sense of connection as we navigated an unfamiliar path of accelerated discovery together. It is delicately exciting to be at the brink of discovery. But with highs come deep lows, and for the first time, I understood how effortful it is build the knowledge and evidence necessary to rigorously support a single sentence in a medical textbook. The entire profession of medicine—physicians and trainees alike—watched, rapt, as vaccines for an enigmatic virus were trialed and failed. As pharmacology of cures was embraced and disputed. As public health efforts steered in dizzying directions. As neglected social inequities and their suffocating hold on public health were magnified. While all this unfolded, the same detailed medical knowledge that once seemed onerous to me became a new foundation for evolving clinical practice. I spent my medical school years trudging through clinical knowledge, feeling troubled by its magnitude yet hoping that studying it all might be enough one day. Lifelong learning, of course, seemed necessary but felt like a remote habit that might be relevant years after graduation. A pandemic nudges change. For me, this pandemic helped inspire newfound gratitude for the arduous nature of discovery, the privilege of collective scholarship, and the necessity of humble perspective in meaningful learning. Acknowledgments: The author would like to thank her mentor Donald Redelmeier for continuing to inspire curiosity, optimism, and integrity as a learner.
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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.014 | 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".