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Record W1548925897 · doi:10.1186/s13104-015-1199-4

Ultra percutaneous dilation tracheotomy vs mini open tracheotomy. A comparison of tracheal damage in fresh cadaver specimens

2015· article· en· W1548925897 on OpenAlexafffundabout
Khalid Al‐Qahtani, Jon Adamis, Jennifer Tse, Jeffrey R. Harris, Tahera Islam, Hadi Seikaly

Bibliographic record

VenueBMC Research Notes · 2015
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsAlberta Health ServicesUniversity of Alberta HospitalUniversity of Alberta
FundersUniversity of Alberta
KeywordsTracheotomyMedicinePercutaneousTracheal StenosisCadaverDilation (metric space)SurgeryAirway

Abstract

fetched live from OpenAlex

BACKGROUND: To compare the ultra percutaneous dilation tracheostomy (PDT) and mini open techniques (MOT) in randomized fixed and fresh cadavers. Assess degrees of damage to tracheal cartilage and mucosa via tracheal lumen and external dissection. METHOD: Comparative cadaver study was performed, tracheostomy was placed in 36 cadavers (16 fixed, 20 fresh) from July 2004 to December 2004, in University of Alberta, Canada. PDT (size 7) were placed by intensivist and MOT (size 7) otolaryngologist. Both fixed and fresh cadavers were randomized. Evaluation was done according to gender, ease of landmark, mucosal and cartilage injuries. RESULTS: Significant differences in mucosal injury (7 of 9 in UPDT VS 0 of 7 in MOT, p value 0.008), and cartilage injury (8 of 9 in UPDT VS 1 of 7 in MOT p value 0.012) were seen in fixed cadavers; and in fresh cadavers, mucosal injury (5 of 10 in UPDT VS 0 of 10 in MOT, p value 0.043), and cartilage injury (5 of 10 in UPDT VS 0 of 10 in MOT, p value 0.043). CONCLUSIONS: PDT resulted in severe damage to mucosa and cartilage, that might contribute to subglottic stenosis preventing decannulation. Considering the injury, MOT has better outcome than UPDT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.331
GPT teacher head0.477
Teacher spread0.147 · 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 teacher head, 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

Citations4
Published2015
Admission routes3
Has abstractyes

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