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Record W3081949590 · doi:10.7759/cureus.10071

Successful Laparoscopic Repair of Posttraumatic Diaphragmatic Injury

2020· article· en· W3081949590 on OpenAlexaff
Khuram Khan, Saqib Saeed, Farhana Iqbal, Leaque Ahmed, Brian Donaldson

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicCongenital Diaphragmatic Hernia Studies
Canadian institutionsColumbia College
Fundersnot available
KeywordsMedicineStab woundDiaphragmatic ruptureDiaphragmatic breathingLaparotomyThoracic cavitySurgeryDiaphragmatic herniaPenetrating traumaThoracic traumaHerniaBlunt

Abstract

fetched live from OpenAlex

Diaphragmatic injuries can be a direct result of penetrating thoracoabdominal trauma such as gunshot or stab wounds. Diaphragmatic rupture can lead to herniation of intra-abdominal organs into the thoracic cavity. Diagnosis can be difficult as the results of a physical exam can be unremarkable. A CT scan of the chest is diagnostic for diaphragmatic injuries. In most emergency cases, diaphragmatic injuries are managed with laparotomy where CT was diagnostic. We report a rare case of a 25-year-old man with right diaphragmatic injury sustained after a stab wound to the right liver managed successfully with laparoscopy.

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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.031
GPT teacher head0.294
Teacher spread0.264 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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