New report preparation system for endoscopic procedures using speech recognition technology
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
Abstract Background and study aims We developed a new reporting system based on structured data entry, which selectively extracts only endoscopic findings from endoscopists’ oral statements and automatically inputs them into appropriate columns in real time during endoscopic procedures. Methods We compared the time for endoscopic procedures and report preparation (ER time) by using an esophagogastroduodenoscopy simulator in three groups: one preparing reports using a mouse after endoscopic procedures (CE group); a second group preparing reports by using voice alone during endoscopic procedures (SR group); and the final group preparing reports by operating the system with a foot switch and inputting findings using voice during endoscopic procedures (SR + FS group). For the SR and SR + FS groups, we identified the recognition rates of the speech recognition system. Results Mean ER times for cases with three findings each were 162, 130 and 119 seconds in the CE, SR and SR + FS groups, respectively. The mean ER times for cases with six findings each were 220, 144 and 128 seconds, respectively. The times in the SR and SR + FS groups were significantly shorter than that in the CE group (P < 0.017). The recognition rate of the SR group for cases with three findings each was 98.4 %, and 97.6 % in the same group for cases with six findings each. The rates in the SR + FS group were 95.2 % and 98.4 %, respectively. Conclusion Our reporting system was demonstrated to allow an endoscopist to efficiently complete the report in real time during endoscopic procedures.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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