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Record W3007939271 · doi:10.1093/jcag/gwz047.128

A129 THE VALUE OF REPEAT MANOMETRIC TESTING

2020· article· en· W3007939271 on OpenAlexaff
Arun Pandey, A Liu, Michelle Buresi, Manish Gupta, Yasmin Nasser, Michael Curley, D Y Li, Christopher N. Andrews, Matthew Woo

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAchalasiaHigh resolution manometryMedicineLogistic regressionInternal medicineMedical diagnosisEsophageal motility disorderSurgeryRadiologyEsophagus

Abstract

fetched live from OpenAlex

Abstract Background While motility disorders may evolve over time, there is scant guidance around the role of repeat high-resolution esophageal manometry (HRM). Given the invasive nature of HRM and the implications on financial cost and patient discomfort, it is obvious that the emphasis should be on minimizing unnecessary repeat examinations. However, there are no recommendations around indications or timing of repeat HRM. Aims We aimed to determine the outcomes in patients who underwent repeat manometry and look for predictors of progression to achalasia or major motility disorder. Methods Consecutive reports from HRM studies performed between Aug 2013 – May 2017 were retrospectively analyzed. All patients with ≥ 2 HRM studies were included. Studies without a Chicago classification diagnosis were excluded. Chi-squared analysis was performed to determine if initial HRM diagnosis was associated with change in diagnosis on follow-up HRM. Initial and follow-up manometric parameters were compared with paired T-tests. Binary logistic regression analysis was performed to look for predictors of progression to achalasia or major motility disorder. Results 134 patients underwent ≥ 2 HRM studies. Initial diagnoses were IEM (45 patients [33.6%], EGJOO (34 [25.4%], absent peristalsis (18 [13.4%], achalasia (11 [8.2%], DES (4 [3.0%]), and JH (3 [2.2%]; 29 (14.2%) of patients had a normal HRM. 109 (81.3%) patients underwent 2 HRM, 18 (13.4%) 3 HRM, 4 (3%) 4 HRM, and 3 (2.2%) 5 HRM. The final follow-up HRM occurred after a median 496 [80 – 1823] days. 72 (53.7%) of patients had no change from their initial diagnosis. Patients with an initial diagnosis of DES were significantly more likely to have a change in diagnosis on the final follow-up (3 normal:1 IEM) (p = .043). No other classes reached significance. Patients with IEM had a significantly higher mean DCI (395.1 [0 - 3248] vs 790.8 [0 – 10715.0], p = .006) and IRP (4.5 [-10.4 – 14.2] vs [6.6 [-6.2 – 21.0], p = .017) on their follow-up HRM. 4 patients without achalasia (3 EGJOO:1 IEM) on their index HRM had a diagnosis of achalasia on their final HRM. The median IRP in non-achalasia patients with a diagnosis of achalasia on final HRM (22.3 [8.4 – 30.7] was significantly higher than those without a diagnosis of achalasia on final HRM (6.6 [-10.4 – 39.8]) (p = .013); however no manometric criteria or initial HRM diagnoses predicted progression to achalasia or major motility disorder on binary logistic regression analysis. Conclusions In most patients, repeat manometry did not change the manometric diagnosis. Patients with DES were significantly likely to have their diagnosis change with repeat HRM, and most of these patients had normalization of their HRM. Manometric parameters in IEM appear to improve over time. This finding could reflect interval therapy, or shed some light on the natural history of this disease. Funding Agencies None

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.021
GPT teacher head0.240
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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