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Record W4414049964 · doi:10.1093/dote/doaf061.216

351. PARA-ANASTOMOTIC FLUID ANALYSIS AFTER ESOPHAGECTOMY: PRELIMINARY RESULTS FROM A PILOT CLINICAL TRIAL

2025· article· en· W4414049964 on OpenAlexaff
Margaret Lasonde, Jenny Bui, Nour Helwa, Manaswi Sharma, Alexander Frickie, Mohamed A. Helwa, Andrew C. Chang, David D. Odell, Jules Lin, Kiran H. Lagisetty, Chigorizim Ekeke, Rishindra M. Reddy

Bibliographic record

VenueDiseases of the Esophagus · 2025
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsEmmanuel Bible College
Fundersnot available
KeywordsLeakComplicationAnastomosisEsophagectomyCatheterDrainageClinical trialProspective cohort study

Abstract

fetched live from OpenAlex

Abstract Background Anastomotic leak is a serious complication following esophagectomy, with risk factors including poor conduit blood flow and cervical location of the anastomosis. Leaks occur in 10–25% of patients and lead to increased morbidity, additional healthcare utilization, and a mortality rate of 7–35%. Fluid acidity (pH) and electrical conductivity (EC) have been shown to be predictors of leak after colonic anastomoses. We hypothesize that pH and EC, from drain output, can enable early prediction of anastomotic leaks following esophagectomy. Methods A prospective pilot study of patients undergoing esophagectomy at a single academic institution was performed. A portable, non-invasive biosensor system was attached inline between a standard anastomotic drainage catheter and evacuator bulb. The sensor continuously monitored drainage fluid characteristics including pH and electrical conductivity (EC), viewed in real-time on a bedside monitor. Preliminary data are summarized with Lowess plots for visual comparison and summary statistics (average, standard deviation). Results Thirteen patients have been evaluated: 9 neck drains (from 7 transhiatal and 2 McKeown esophagectomies) and 7 chest drains (from 4 Ivor Lewis esophagectomies with 1–2 drains). One patient was diagnosed with a leak, and two patients were diagnosed with clinical neck infections without leak. Average pH from cervical (n = 6) or chest (n = 7) drains without leak/infection was 8.005+/−0.661 and 8.135+/−0.224, respectively (Fig. 1a). Average EC from cervical or chest drains was 11.441+/−1.178mS/cm and 12.691+/−0.538mS/cm, respectively (Fig. 1b). In one patient with leak, the average cervical drain pH was 7.639 (Fig. 1c) and average EC was 12.443 (Fig. 1d). Conclusion Preliminary data suggest that monitoring para-anastomotic electrical conductivity and pH merits continued investigation to predict post-esophagectomy anastomotic leaks, with pilot data showing similar trends (lower pH, higher EC) observed for intraperitoneal bowel anastomotic leaks.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.386
Teacher spread0.338 · 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 designNon-randomized trial
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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