Bibliographic record
Abstract
Social media is difficult to explain to a physician who has never used it. The medical literature on its pitfalls and abuses has overshadowed its positive applications and made many physicians wary of it. While I was initially reluctant to develop my own presence on social media, since embracing it as a tool for teaching and learning I have developed a different perspective. I see it as a tool that can be used positively or negatively. Much like a megaphone, it can amplify our voice so that the impact of our work can extend beyond the borders of our institutions and countries. Aided by the guidance and support of mentors who used social media before and alongside me, it has helped me to become a more competent, professional, engaged, and impactful physician. Within this article I will share my story to illustrate the many ways that social media can be used to enhance the profession of medicine.
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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.040 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.017 | 0.035 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".