Assessing Treatment Outcomes in Achalasia Using 4-Dimensional High-resolution Impedance Manometry
Bibliographic record
Abstract
Background/Aims: Assessment of treatment response of achalasia often involves multiple procedures. We aim to investigate innovative metrics based on 4-dimensional high-resolution impedance manometry (4D HRM) to assess treatment response in achalasia patients. Methods: Patients with achalasia treated by pneumatic dilation or myotomy who underwent follow-up evaluations were included. All patients completed high-resolution impedance manometry before and after treatment. 4D HRM analysis based on developed python program measured clearance ratio, intrabolus pressure (IBP), maximum esophagogastric junction diameter, and distensibility index. Good treatment outcomes were defined as barium column height of < 5 cm at 5 minutes on timed barium esophagram (TBE) and Eckardt score ≤ 3. Results: Fifty-three patients with achalasia were included: 40% type I, 51% type II, and 9% type III. Change of clearance ratio and IBP on 4D HRM had superior performance in predicting abnormal TBE at 5 minutes (area under the receiver operating characteristic [AUROC] curve, 95% confidence interval: 0.76, 0.59-0.93; 0.74, 0.57-0.92). The combination of clearance ratio (increase with a threshold of 0.1) and IBP (reduction with a threshold of 8.9 mmHg) had a high positive predictive value for normal TBE outcome (93%), and a modest negative predictive value for abnormal TBE outcome (73%). Receiver operating characteristics of metrics related to poor symptomatic outcome only yielded AUROCs (95% CI) of 0.82 (0.68-0.96) for esophageal hypervigilance and anxiety scale posttreatment. Conclusions: IBP and clearance ratio help to identify abnormal barium retention in patients after treatment. 4D manometry can be an alternative or complementary approach to characterize and assess treatment response of Achalasia, in additional to TBE or functional lumen imaging probe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".