Low-bandwidth telemedicine for pre- and postoperative evaluation in mobile surgical services
Bibliographic record
Abstract
Low-bandwidth telemedicine was used for the pre- and postoperative evaluation of patients treated by a mobile surgery service in remote Ecuador. Realtime and store-and-forward telemedicine was employed, using PCs connected via the ordinary telephone network. Between February 2002 and July 2003, 144 patients were studied preoperatively and 50 postoperatively. It was possible to establish 20 satisfactory preoperative realtime connections, which allowed good-quality, simultaneous audiovisual transmission. Thus, there were 124 preoperative assessments done by store-and-forward telemedicine and 50 postoperative assessments. Diagnoses and management plans made by a surgeon using telemedicine were compared with those made independently by a second surgeon, who saw the patient face to face. Due to poor quality of the transmitted images, 43 patients were excluded from the preoperative study and 13 from the postoperative study. In the 101 preoperative evaluations, there was agreement in 78 cases (77%); in the 37 postoperative evaluations, there was agreement in 36 cases (97%). Telemedicine may reduce the time required on site for preoperative planning, and may provide reliable postoperative surveillance, thus improving the efficiency of mobile surgery services.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".