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Record W2000496331 · doi:10.4021/gr263w

Management of Esophageal Perforation in Adults

2011· review· en· W2000496331 on OpenAlexvenueno aff
Lileswar Kaman

Bibliographic record

VenueGastroenterology Research · 2011
Typereview
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePerforationEsophagusSurgeryMediastinumMortality rateEtiologyInternal medicine

Abstract

fetched live from OpenAlex

Perforation of esophagus in the adult is a very morbid condition with high morbidity and mortality. The ideal treatment is controversial. The main causes for esophageal perforation in adults are iatrogenic, traumatic, spontaneous and foreign bodies. The morbidity and mortality rate is directly related to the delay in diagnosis and initiation of optimum treatment. The reported mortality from treated esophageal perforation is 10% to 25%, when therapy is initiated within 24 hours of perforation, but it could rise up to 40% to 60% when the treatment is delayed beyond 48 hours. Primary closure of the perforation site and wide drainage of the mediastinum is recommended if perforation is detected in less than 24 hours. Treatment option for delayed or missed rupture of esophagus is not very clear and is controversial. Recently a substantial number of patients with esophageal perforation are being managed by nonoperative measures. Patients with small perforations and minimal extraesophageal involvement may be better managed by nonoperative treatment Major prognostic factors determining mortality are the etiology and site of the injury, the presence of underlying esophageal pathology, the delay in diagnosis and the method of treatment. For optimum outcome for management of esophageal perforations in adults a multidisciplinary approach is needed.

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)0.000
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.124
GPT teacher head0.428
Teacher spread0.304 · 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
GenreReview

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

Citations173
Published2011
Admission routes1
Has abstractyes

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