Comparing ‘Twitter’ polls results with an online survey on surgeons perspectives for the treatment of rectal cancer
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction Traditional surveys (including phone, mail and online) can be valuable tools to obtain information from specific communities. Social media apps such as Twitter are being increasingly adopted for knowledge dissemination and research purposes. Twitter polls are a unique feature which allows for a rapid response to questions posed. Nonetheless Twitter does not constitute a validated survey technique. The objective was to compare the similarities of Twitter polls in describing practice patterns for the treatment of rectal cancer. Methods A survey on the management of rectal cancer was designed using modified Delphi methodology. Surgeons were contacted through major colorectal societies to participate in an online survey. The same set of questions were periodically posted by influencers on Twitter polls and the results were compared. Results A total of 753 surgeons participated in the online survey. Individual participation in Twitter ranged from 162 to 463 responses. There was good and moderate agreement between the two methods for the most popular choice (9/10) and the least popular choice (5/10), respectively. Discussion It is possible that in the future polls available via social media can provide a low-cost alternative and an efficient, yet pragmatic method to describe clinical practice patterns. This is the first study comparing Twitter polls with a traditional survey method in medical research. Conclusions There is viable opportunity to enhance the performance of research through social media, however, significant refinement is required. These results can potentially be transferable to other areas 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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it