Machine Learning Approach to Predict Treatment Outcome Using Shockwave Lithotripsy in Management of Urinary Stone
Bibliographic record
Abstract
In Ontario, shock wave lithotripsy (SWL) is a regionalized resource and St. Michaels Hospital is one of only three centers in the province offering this service. As such, many of the patients travel a great distance to receive this noninvasive treatment. Our objective is to implement ensemble learning technique to predict treatment outcome based on the patients demographic information and stone characteristics. In order to construct a rigorous machine learning model that can be confidently applied to assist in decision making process, we built our model based on the whole dataset of patients ages over 18 for the years from 1998 to 2016. Our objective is to build a classification model to predict treatment outcome using SWL prior to making any decision on treatment modality. The success or failure was based on having retreatment plan for the same patient within less than 90 days of initial treatment. We also compared six machine learning algorithms performance on dataset in terms of their accuracy using t-test with 95% confidence interval. \nIn addition, we performed a retrospective comparison of three shock wave lithotripsies (SWL) that has been used in SMH during the past two decades in terms of their successfulness. Furthermore, we looked at changing trends over time in terms of stone size, location, and patient BMI, and site of origin, gender, age, etc.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".