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Record W3036347347 · doi:10.21037/ales-19-241

Management of complications after paraesophageal hernia repair

2021· article· en· W3036347347 on OpenAlexaff
Abraham Botha, Francesco Di Maggio

Bibliographic record

VenueAnnals of Laparoscopic and Endoscopic Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineHernia repairHerniaSurgeryGeneral surgery

Abstract

fetched live from OpenAlex

Laparoscopic paraesophageal hernia (PEH) repair can be performed safely in expert hands. However, it is a complex operation carrying significant risk of peri-operative morbidity and mortality. Careful intra-operative correctional techniques and prompt return to theatre for early post-operative complications result in a satisfactory outcome. Capnothorax and pneumothorax should be dealt with immediately by lowering insufflation pressure, aspiration and drain placement. Hemodynamic instability from cardiovascular injury or bleeding mandates an early return to theatre. Intra-operative perforation of the esophagus or stomach is best avoided, but it can be successfully repaired. Early acute dysphagia warrants a return to theatre for correction while delayed dysphagia can in some patients be treated by dilatation. Asymptomatic hiatus hernia recurrence does not require surgery, but symptomatic and complicated hernias can be re-repaired. Some life-threatening complications such as acute gastric dilatation, aortic fistula, gastric necrosis and perforation can occur months, and even years, after the procedure. Other complications that can severely impair quality of life in the longer term include esophago-gastric junction stenosis, hiatus hernia recurrence, delayed gastric emptying and excess intestinal gas. Longer term revisional surgery for life-threatening or life-impairing complications can be done safely by expert surgeons but has a lower chance of success than primary surgery.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.329
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2021
Admission routes1
Has abstractyes

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