Endoscopic Prediction of Achalasia: Putting the <scp>CART</scp> Before the <scp>CARS</scp>
Bibliographic record
Abstract
BACKGROUND AND AIMS: Endoscopy can detect features indicative of esophageal dysmotility, but standardized approaches for diagnosing achalasia based on these findings remain limited. Recently, the CARS score was developed to address this gap. This study aimed to evaluate the diagnostic utility of endoscopy in identifying achalasia, using the STARD framework and current reference standards. METHODS: Adult patients with esophageal symptoms were prospectively enrolled from 2018 to 2023 and evaluated using endoscopy, esophageal manometry, FLIP panometry, and barium esophagram. The CARS score was assigned to endoscopic videos by two raters blinded to other clinical details. The diagnostic accuracy of the CARS score for predicting achalasia, based on Chicago Classification v4.0, was assessed through two interpretation methods: binary cutoffs for the total score and a classification tree model. RESULTS: 316 patients were included: 115 patients with achalasia (36%), 113 with normal motility (36%), and 88 with other manometric findings (28%). A CARS score ≥ 4 demonstrated 72% sensitivity and 99% specificity for achalasia, while a score ≥ 3 had 83% sensitivity and 96% specificity. The optimal classification tree had three levels (resistance score at the top, followed by anatomy and content scores, with hernia presence at the bottom) and had a sensitivity of 90% and a specificity 92% for achalasia. CONCLUSION: Endoscopy can accurately identify achalasia with high specificity using the CARS score. While motility testing to confirm an achalasia diagnosis remains essential prior to therapy, a high CARS score may help in the early identification of achalasia, especially in settings where motility testing is not readily available.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".