Neurosurgery and the rise of academic social media: what neurosurgeons should know
Bibliographic record
Abstract
ocial media has evolved to take on a major role in the personal and professional lives of people across the world.As of 2016, Pew Research Center data es timates that 86% of US adults used the Internet, of whom 79%, 24%, 32%, and 29% used Facebook, Twitter, Insta gram, and LinkedIn, respectively.12 In parallel, social media use among clinicians, including neurosurgeons, neurosurgical departments, and neurosurgical journals, has increased exponentially over the past 3 years.2,4,6,10,11,19 Given this dramatic rise and the increasing emphasis be ing placed on social media in professional networking, pa tient recruitment and education, and the dissemination or discussion of new scientific knowledge, our objective was to highlight the potential benefits and risks of developing a social media presence for members of the neurosurgical community in particular, as well as offer a preliminary guide for the novice social media user.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 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".