Bibliographic record
Abstract
To the Editor: The use of social media for education and networking flourished during the COVID-19 pandemic. As international medical graduates (IMGs), it has positively impacted our journeys in pathology. This letter discusses our experience with collaborating via social media and highlights specific issues associated with such endeavors. #PathTwitter is a thriving X (formerly known as Twitter) community of pathologists that has facilitated many successful collaborations. For instance, we met virtually during a project centered around an immunostain ubiquitous in pathology laboratories. Interestingly, while our mentor was a professor in the United States, the 2 of us were located in Singapore and Canada, respectively. Additionally, the 3 of us had never met in person and knew each other only through interactions on X. Fortunately, that did not hamper our collaboration, even though we were working from 3 time zones. Our experience has been similar collaborating with other medical students, residents, or faculty via social media. In contrast to conventional mentor–mentee relationships that involve periodic in-person meetings, virtual mentorship offers distinct advantages. For IMGs, it provides opportunities to obtain advice from trainees or attending physicians about residency or fellowship applications while remaining in their home country. For physicians in practice, it facilitates access to expertise outside of one’s institution for research projects or challenging cases. Such an arrangement also cuts the cost of travel and accommodations, reducing the financial burden on young professionals looking to build their careers. Although sustaining a mentor–mentee relationship over social media platforms may appear convenient, practical considerations, such as time zone differences and technological compatibility between mentor and mentee, must be taken into account when organizing meetings. In such instances, meetings are planned with intention, as opposed to spontaneous drop-ins that in-person settings allow. Eventually, relationships built over social media platforms may yearn for the warmth of face-to-face interactions. Sometimes, the mentor and mentee meet in person after a virtual interaction. On X, this phenomenon has given rise to the popular acronym “MOTTIRL,” which stands for “met-on-Twitter-then-in-real-life.” It captures the ineffable feeling of warmth when the physical interaction between mentor and mentee materializes after working with each other virtually for a while—for months or even years. All in all, virtual mentorship may pose unique challenges, but our personal experiences have shown it can be a great equalizer, allowing young professionals to access expertise remotely and experts to work with talent in otherwise hard-to-reach areas. Lavisha S. Punjabi, MBBS Senior resident, Anatomical Pathology, Singapore General Hospital, Singapore; email: [email protected]; X (formerly Twitter): @punjbiopsy; ORCID: https://orcid.org/0000-0003-0479-1690 Abhimanyu Tushir, MD Resident physician, Anatomical Pathology and Clinical Pathology, Temple University Hospital, Philadelphia, Pennsylvania
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".