Changes in weight following peroral endoscopic myotomy: results from a North American center
Bibliographic record
Abstract
Abstract Background and study aims Weight loss is a feature of untreated achalasia. While weight gain after achalasia treatment is expected, little data exist about the extent of it, particularly after peroral endoscopic myotomy (POEM). We aimed to describe the changes in weight after POEM. Patients and methods A retrospective analysis was performed of patients from March 2016 to March 2020 undergoing POEM by an experienced operator at an academic referral center in Kingston, Ontario, Canada. Data examined included pre- and post-POEM body mass index (BMI), procedural characteristics, and Eckardt scores. Results Ninety-two patients underwent POEM during the study period, for 77 of whom follow-up data were available including BMI. Prior to POEM, the median BMI was 26. Six patients (7.8 %) were considered to be underweight, 24 (31.2 %) were normal, 26 (33.8 %) were pre-obese, and 21 (27.2 %) were obese. At a median follow-up of 15 months, the clinical success rate was 95 %. Patients in the normal weight and pre-obesity categories had a significant increase in weight post-POEM. Of the underweight patients, two (33.3 %) moved up to a normal weight category and two (33.3 %) became pre-obese. In the normal BMI category, eight (37.5 %) became pre-obese and one patient (4.2 %) became obese. Ten of the 26 pre-obese patients (38.4 %) were obese post-POEM. Seven of the obese patients (33.3 %) reached Class III (morbid) obesity at follow-up. Conclusions Patients may experience a marked increase in BMI following POEM, with over one-third of patients becoming pre-obese, obese or morbidly obese at follow-up in our study in a North American population. Further study is required to analyze weight gain patterns post-POEM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, 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".