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Translaryngeal Tracheostomy: Experience of 340 Cases

2003· article· en· W2060691504 on OpenAlexaff
Michael D. Sharpe, Lorne S. Parnes, John Drover, Chris Harris

Bibliographic record

VenueThe Laryngoscope · 2003
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsKingston General HospitalWestern UniversityCARE CanadaLondon Health Sciences Centre
Fundersnot available
KeywordsMedicinePartial thromboplastin timeIntensive care unitComplicationSurgeryAnesthesiaIntensive care medicinePlateletInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the authors' initial experience with a new and innovative dilational translaryngeal tracheostomy bedside technique. STUDY DESIGN: A prospective documentation of 340 patients who received an elective translaryngeal tracheostomy in a multidisciplinary, tertiary care intensive care unit during a 45-month period. RESULTS: All translaryngeal tracheostomy procedures but one were completed successfully; one was aborted because of bleeding from a thyroid vein. Minor perioperative complications occurred in 42% of patients, which caused no adverse effects. The most common complication was arterial desaturation occurring in 17% of patients; this was short-lived, and the lowest saturation was 79%. Blood loss was minimal (<5 mL) in all but one case, despite an elevated international normalized ratio (INR) and partial thromboplastin time in 42% and 41% of patients, respectively, and a low platelet count in 13% of patients. CONCLUSIONS: Translaryngeal tracheostomy is a safe and reliable technique and can also be used in patients with unstable cervical spines and bleeding diathesis. It has become the authors' procedure of choice for an elective bedside tracheostomy in the intensive care unit.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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 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

Citations7
Published2003
Admission routes1
Has abstractyes

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