MétaCan
Menu
Back to cohort

A simple, inexpensive, life-saving way to perform iterative laparotomy in patients with severe intra-abdominal sepsis

2001· article· en· W2066155304 on OpenAlexaff
A. Doyon, Ghislain Devroede, D. Viens, Satoshi Saitô, Vincent Échavé, Mathieu-Robert Sauvé, Matthew J. Martin, J Poisson

Bibliographic record

VenueColorectal Disease · 2001
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineSurgeryIncisional herniaLaparotomyAbdomenPound (networking)SepsisAcute pancreatitisAbdominal compartment syndromeHernia

Abstract

fetched live from OpenAlex

Between 1 June 1993 and 31 December 1998, 17 patients underwent temporary abdominal closure with 3L urological irrigation bags, because in most cases, there was massive sepsis leading to the conclusion that primary closure was not advisable. Indicative of the seriousness of these conditions, Apache score averaged 19 (range 10-30). The technique consisted of suturing a double thickness of irrigation bags to each side of the wound, and joining the two bags in the midline with running sutures. Abdominal lavage with large quantities of fluid was performed every other day. This type of closure was used for a mean duration of 15 days. Mean length of hospitalization was 60 days. There were only three deaths (17.6%). No incisional hernia occurred after the iterative laparotomies. Deleting patients with acute pancreatitis would have reduced the death rate to only 7%. A 3L urological irrigation bag costs pound 11.60 (24.40 dollars CAN) while a Marlex mesh costs pound 81.40 (171.00 dollars CAN). We conclude that the usage of 3L urological plastic bags is a simple, safe and efficient method for temporary closure of the abdomen.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.250
Teacher spread0.239 · 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 designCase report
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

Citations16
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueColorectal DiseaseSame topicAbdominal Surgery and ComplicationsFrench-language works237,207