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Record W2310309545 · doi:10.15406/mojs.2015.02.00030

Percutaneous Tracheostomy in a Super Obese Patient: A Case Report

2015· article· en· W2310309545 on OpenAlexaff
Abdollah Behzadi

Bibliographic record

VenueMOJ Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPercutaneousMedicineIntensive care medicineGeneral surgerySurgery

Abstract

fetched live from OpenAlex

Introduction: Obesity is a growing global epidemic and is associated with morbidity and mortality. Airway management of morbidly obese patients requiring long-term ventilation is scarce. This is the first case report in literature to describe a percutaneous tracheostomy technique performed on a super obese patient (Body Mass Index = 87 kg/m2). Case presentation: A 31-year old gentleman, weighing 640 pounds, with a history of congestive heart failure, renal failure and chronic obstructive pulmonary disease, presented to the emergency department with decreased level of consciousness and admitted to the intensive care unity with respiratory failure. He required long-term mechanical ventilation however given his BMI and co-morbidities, the best approach was unclear. After careful inspection of the anatomy, a percutaneous tracheostomy was safely performed in this patient. Conclusion: Performing a percutaneous tracheostomy is feasible and safe in selected super obese patients whose neck anatomy is favorable-particularly the palpation of the cricoid cartilage and the proximal tracheal rings. Appropriate positioning in these patients, while difficult, is also a sentinel step in the success of this technique.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0090.005
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 head0.041
GPT teacher head0.267
Teacher spread0.225 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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