Bibliographic record
Abstract
Virtual pathology is a very important tool that can be used in several ways, including interconsultations with specialists in many areas and for frozen sections. We considered in this work the use of Windows Live Messenger and Skype for image transmission. The conference was made through wide broad internet using Nikon E 200 microscope and Digital Samsung Colour SCC-131 camera. Internet speed for transmission varied from 400 Kb to 2.0 Mb. Both programs allow voice transmission concomitant to image, so the communication between the involved pathologists was possible using microphones and speakers. A live image could be seen by the receptor pathologist who was able to ask for moving the field or increase/diminish the augmentation. No phone call or typing required. The programs MSN and Skype can be used in many ways and with different operational systems installed in the computer. The capture system is simple and relatively cheap, what proves the viability of the system to be used in developing countries and in cities where do not exist pathologists. With the improvement of software and the improvement of digital image quality, associated to the use of the high speed broad band Internet this will be able to become a new modality in surgical pathology.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".