Anorectal symptom management in pregnancy: development of a severity scale.
Bibliographic record
Abstract
BACKGROUND: Anorectal conditions are very common and under-diagnosed in pregnancy, with severe implications on quality of life. Presently no validated scale is available to quantify the severity of symptoms and their response to therapy. The objective of this study was to create and validate a scale for symptoms associated with anal/rectal conditions. METHODS: Patients attending a colorectal clinic were assessed twice, for severity of anorectal symptoms; once by the new questionnaire-ColoRectal Evaluation of Clinical Therapeutics Scale (CORECTS)--followed by a direct examination by a proctologist. Linear regression analysis was performed to correlate the clinician's and CORECTS scores. In parallel, 209 pregnant women with hemorrhoids were assessed using CORECTS before and after treatment with Proctofoam-HC®. We evaluated whether scores' improvement corresponded to changes in quality of life. RESULTS: There was a significant concordance between each component of the CORECTS scale as well as impact on quality of life, with direct clinical examination of a proctologist. Significant reduction in symptoms, as measured by the scale following use of Proctofoam-HC® highly correlated with changes in quality of life before and after treatment. CONCLUSION: CORECTS is a reliable tool in capturing the severity of symptoms associated with colorectal symptoms in pregnancy and is highly sensitive in detecting changes in symptom severity following treatment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".