Social media in pathology and laboratory medicine: A systematic review
Bibliographic record
Abstract
The use of social media platforms in pathology and medical laboratory science has increased in recent years, revolutionizing the way professionals in these fields interact, disseminate information, and collaborate. To gain an understanding of the current landscape regarding social media use in pathology and medical laboratory science, a novel systematic review was conducted. A search of PubMed, Medline, Embase, and Scopus was performed to identify articles evaluating social media use within pathology and medical laboratory science. Articles published in English within the previous 10 years were searched on December 22, 2022. Inclusion criteria were articles containing information regarding social media utility in pathology or laboratory medicine and related articles that mentioned specific hashtags for pathology. The review process involved analyzing the social media platforms referenced, hashtags mentioned, and the presence of international authors as key endpoints of interest. 802 publications were identified; 54 studies met inclusion criteria. Subspecialties represented were considered, but none were found to be statistically significant. X/Twitter (n = 42) was the most discussed social media platform. The top hashtags discussed were #pathJC (5.1%), #dermpathJC (4.2%), #USCAP2016 (3.4%), and #PathBoards (3.4%). Analysis of these articles provides insights into current trends, including the social media platforms referenced, hashtags used, and involvement of international authors. This review will contribute to a deeper understanding of the role and impact of social media in these fields, highlighting opportunities and challenges for future research and practice in pathology and lab 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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".