The severity of renal colic pain: Can it be predicted?
Bibliographic record
Abstract
INTRODUCTION: We aimed to determine whether there was a relationship between the perception of renal colic pain and different psychosocial and physiological factors. METHODS: Between May 2021 and July 2022, we prospectively analyzed 320 patients over the age of 18 who were diagnosed with renal colic occurring unilaterally and secondary to a single kidney stone of any size. Body mass index (BMI), education level, hospital anxiety and depression scale (HADS), somatosensory amplification scale (SAS), and the visual analog scale (VAS) features of stone (diameter, Hounsfield value, and localization) and degree of hydronephrosis were analyzed. Correlation analysis of VAS score and these parameters were completed with Spearman's test. The regression analysis was used to determine the predictive factors of severe pain. RESULTS: There was no significant difference found between sex and VAS scores of colic pain (p=0.122). We found a significant correlation between VAS score and localization of kidney stone, degree of hydronephrosis, and anxiety level of patients. High grade of hydronephrosis and high anxiety level were found to be associated with high VAS scores (p<0.001 and p=0.035, respectively). It was shown that SAS and level of depression did not correlate with pain. Only a high degree of hydronephrosis was found to be a predictive factor for severe pain (p<0.01). CONCLUSIONS: The patient's high anxiety level and a high degree of hydronephrosis were positively correlated with renal colic pain caused by kidney stones. With this study, the severity of pain in patients with a high degree of hydronephrosis and high anxiety can be predicted and may be a criteria to select suitable treatment to reach faster response.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".