Bibliographic record
Abstract
Family medicine, Wisdom in healthcare hank you for listening, for caring.Thank you for your advice, you are wise beyond your years," she said as the tears streamed down her face.The room was hot and heavy with raw emotion, swirling like dust in the rays of the mid-afternoon sun."I will see you back here next month," I said thoughtfully as she stood up, collected herself and stepped out of my office.Me… wise?Like many medical trainees, imposter syndrome followed me around like my shadow.Selfdoubt caused me to question almost every diagnosis and treatment decision for most of my medical training.The evaluations from my attendings continued to feed the beast of self-doubt.I felt overwhelmed by the array of possibilities and investigatory options for any given complaint in a patient.How do I know the right course of action to choose?I have since learned that there is not only one right way to manage a patient.Ask a group of five attending physicians questions about how to manage a case and you will have five different answers.I was so worried about selecting the "right answer"-as though life were some sort of multiple-choice question-that I was never able to select MY answer.I was only able to develop that "T Wisdom in healthcare: finding wisdom in unexpected places Laura Sang 18
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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".