Preliminary Assessment of a Renal Tumor Materials Model
Bibliographic record
Abstract
PURPOSE: To evaluate a materials model for laparoscopic guided cryotherapy or radiofrequency tissue ablation (RFA) of kidney tumors through expert surgeon assessment. MATERIALS AND METHODS: During the inaugural American Urological Association 2010 Tissue Ablative course content, validity testing of a renal tumor model was undertaken. Five expert faculty in cryotherapy and RFA techniques for renal tumors performed laparoscopic ultrasonography (US) examination of the tumor model. They performed US guided placement and activation of the treatment probe into the tumor of the model. They completed a questionnaire and rated the quality of the renal tumor model on a 5 point Likert scale. RESULTS: All of the subjects assigned a score of 5 of 5 on the Likert scale regarding the ability to identify the tumor with US, were able to deploy the ablative probe into the model under US guidance, and would recommend the use of this teaching model to residents or fellows. They thought that this tumor model was appropriate for teaching laparoscopic US imaging of a renal tumor during ablative treatment procedures, teaching and practicing laparoscopic US-guided cryotherapy, and teaching and practicing laparoscopic US-guided RFA. CONCLUSION: We have developed a unique model that simulates small kidney tumors that can be used for training surgeons in ablative techniques.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".