Social media in cancer care
Bibliographic record
Abstract
PURPOSE OF REVIEW: To examine the current data supporting use of social media in breast cancer clinical care. RECENT FINDINGS: Although opportunities to utilize social media to increase knowledge have been commonly seized, the opportunity to improve communication among clinicians is lagging. Locally advanced breast cancer (LABC) requires timely coordination of care among many specialists, and presents an excellent scenario for enhanced utilization of current IT strategies. SUMMARY: A systematic review was conducted to assess the use of social media to enhance breast cancer care. In addition, a Web-based search using common search engines and publicly available social media was conducted to determine the prevalence of information and networking pages aimed at patients and clinicians. Over 400 articles were retrieved; 81% focused on delivery of information or online support to patients, 17% focused on delivery of information to physicians, and 1% focused on the use of social media to improve collaboration among clinicians. Web searches retrieved millions of hits, with very few hits relating to improving collaboration among clinicians. Although there is significant potential to utilize current technologies to improve care for patients and improve connectedness among clinicians, most of the currently available technologies focus solely on the delivery of information.
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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".