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Record W2102677630 · doi:10.1097/phm.0b013e31825597b8

Tracheostomy Decannulation and Cough Peak Flows in Patients with Neuromuscular Weakness

2012· article· en· W2102677630 on OpenAlexafffund
Douglas McKim, Ariel Hendin, Carole LeBlanc, Judy King, Catherine Brown, Andrew Woolnough

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2012
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersOttawa Hospital Research Institute
KeywordsMedicineTracheostomy tubeWeaknessAnesthesiaTube (container)LungLung volumesNeuromuscular diseaseSurgeryInternal medicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to examine the relationship between cough peak flows (CPFs) before and after tracheostomy tube removal (decannulation) in patients with neuromuscular respiratory muscle weakness. DESIGN: For 26 patients with occluded tracheostomies (capped or Passy-Muir valve), spontaneous CPF (CPF(sp)), CPF after lung volume recruitment (CPF(LVR)), and CPF after lung volume recruitment and a manually assisted cough (CPF(LVR) + MAC) were measured before and after decannulation. RESULTS: Decannulation resulted in a significant increase (P < 0.001) in CPF of 35.6, 34.5, and 42.6 l/min for CPF(sp), CPF(LVR), and CPF(LVR) + MAC, respectively. In addition, CPF(LVR) or CPF(LVR) + MAC with a capped tracheostomy in place were greater than spontaneous CPF with the tracheostomy tube removed. CONCLUSIONS: Our study suggests that assisted coughing with a capped tracheostomy tube in place can result in higher flows than removing the tube and relying on spontaneous cough alone. Postdecannulation CPF measured at the mouth can be predicted to be at least 34.5 l/min greater than predecannulation values, which may thereby lower the threshold of the CPF indicated for safe decannulation.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.430
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.242
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 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

Citations17
Published2012
Admission routes2
Has abstractyes

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