An Evaluation Framework for Defining the Contributions of Telestration in Surgical Telementoring
Bibliographic record
Abstract
Background: An increasing quantity of research in the domain of telemedicine show a growing popularity and acceptance of care over distance systems among both clinicians and patients. We focus on telementoring solutions, developed for providing remote guidance to less experienced surgeons. Telestration is often regarded as an extra functionality of some telementoring systems. However, we advocate that telestration must be viewed as a core feature of telementoring due to its advantages. Objective: To analyze and define concepts, parameters, and measurement procedures to evaluate the impact of using telestration while telementoring. Methods: A systematic review of research dealing with telestration during remote guidance sessions was performed by querying three major online research databases (MEDLINE, Association of Computing Machinery, and Institute of Electrical and Electronics Engineers) using a predefined set of keywords (“laparoscopy”, “annotate”, “telestrate”, “telestration”, “annotation”, “minimally invasive”, and “MIS”). Results: The keyword-based search identified 117 papers. Following the guidelines for performing a systematic review, only 8 publications were considered relevant for the final study. Moreover, a gap in research defining the impacts of telestration during telementoring was identified. To fill this niche, a framework for analyzing, reporting, and measuring the impacts of telestration was proposed. Conclusions: The presented framework lays the basics for the structured analysis and reporting of telestration applied to telementoring systems. It is the first step toward building an evidence knowledge base documenting the advantages of live video content annotation and supporting the presented connections between the concepts.
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 | 0.196 | 0.345 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.045 | 0.028 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".