Bibliographic record
Abstract
It was early in my first year of medical school that I learned about the “brainbow” - an innovative means of using genetic expression of various fluorescent proteins to colourfully label individual neurons, allowing for the visualization of neural networks within the brain. I was fascinated by the beautiful complexity of these axonal interconnections. In reflection, I drew parallels to my journey through medicine, and the intricacies of navigating human interpersonal relationships. Medical practice includes both the soft and the hard sciences. Academic institutions teach us the hard sciences: the pathophysiology of disease, and the evidence-based practice for diagnosis and management. Over the years of my clinical training, I am learning that much of the soft science of medicine is in the human connection. It is in our ongoing practice of communication and interpersonal skills, and the subsequent relationships that we develop (or sometimes, lose) with our friends, partners, and colleagues, as we face the miracles and the hardships throughout our medical training. It is in our patient interactions: the emotions we share, the empathy we convey, and the rapport that we build in order to provide compassionate patient care. Much like the brain’s neural network, these connections are complex and ever-changing - some connections are strengthened, and others are unfortunately, and perhaps painfully, pruned. My piece “The emotional brainbow” uses fine multicolours of sewn thread to reflect the intricate axonal connections of brain centres involved in processing and expressing emotions: the cortex, the limbic system, the brainstem, and the cerebellum. These crucial structures communicate to facilitate our ability to understand and empathize with others, and contributes towards our continually developing practice of manoeuvering interpersonal relationships. There is a complex, overlapping interplay of these neural connections within the emotion-regulating brain centres, much like the beautifully intricate emotional human connections, which we, as health care professionals, both create and navigate.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 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".