Can the Hounsfield unit predict the success of medically expulsive therapy
Bibliographic record
Abstract
BACKGROUND: We investigate the predictability of medical expulsive therapy (MET) success with alpha blockers based on Hounsfield unit (HU) values and Hounsfield density (HD) values measured by computed tomography (CT) for distal ureteral stones. METHODS: Between July 2011 and May 2012, 44 patients (19 female and 25 male) with 5- to 10-mm stones in the distal ureters were included in this randomized prospective study. Non-contrast CT examinations were performed in these patients. HU and HD values of stones were calculated. Doxazosine, 4 mg/day orally, was administered as a single dose to all patients for MET. RESULTS: Patients were divided into 2 groups. Group 1 included 18 patients (43.9%) with dropped stones with MET. Group 2 included 23 patients (56.1%) with no stone passage with MET. In Group 1, the mean stone size was 7.7 mm, the mean HU was 507, and the HD was 53.04/mm. In Group 2, the mean stone size was 8.25 mm, the mean HU was 625, and the mean HD was 61.54/mm. The HU and HD values in Group 2 were higher than in Group 1. However, there was no statistically significant difference (p = 0.85 and 0.93 for HU and HD, respectively). INTERPRETATION: We found that HU and HD values cannot be used to predict the chances of success for MET. Although the sample size is appropriate for the study, further comparative studies involving more patients are warranted.
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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 | 0.001 | 0.013 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".