MP63-10 DEVELOPMENT OF A CLINICAL DECISION-SUPPORT TOOL FOR CLASSIFICATION OF RENAL MASSES
Bibliographic record
Abstract
and standing in axial and sagittal planes; transverse T1 images at 5-mm thickness, field of view 24 cm, matrix 224x192, sagittal T2 images at 5 mm thickness, field of view 30F cm, matrix 192x160.Axial and sagittal slices were manually segmented (Analyze 12 software) for bladder, urethra, vagina, uterus, cervix, and pelvic floor muscles (PFM), levator ani, piriformis and obturator internus.The pubic bone was segmented as a fixed reference point.Segmentation was based on an amalgamation of knowledge of the anatomic structures and variation in greyscale contrast.3D renderings used surface-rendering.RESULTS: Upright posture reviewed anatomic defects in POP most notable being the levator ani defects not detectable in the supine position with increased size of defects with standing compared to sitting and differences between right and left sides.Fig 1 (A) anterior view showing a 3D model of PFMs, (B) inferior view identifies detachment of the levator ani (arrows).(Segmentation of structures blue ¼ pubic bone, green ¼ ilium and ischium, brown ¼ sacrum, purple ¼ femoral head, pink ¼ levator ani, obturator and symphysis, and light blue ¼ coccyx) CONCLUSIONS: Upright posture unique to MRO allows demonstration of the size and extent of pelvic floor disruption.The new protocol presented allowed for capture of defects in all POP subjects providing clinical information not evident from supine imaging and is feasible for MRO translation of the pelvis into clinical practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".