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Record W2346747777 · doi:10.1155/2016/8715024

The Role of Open Lung Biopsy in Critically Ill Patients with Hypoxic Respiratory Failure: A Retrospective Cohort Study

2016· article· en· W2346747777 on OpenAlexafffund
Abdullah Almotairi, Sharmistha Biswas, Jason Shahin

Bibliographic record

VenueCanadian Respiratory Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcGill University Health CentreMcGill University
FundersMcGill University
KeywordsMedicineRetrospective cohort studyBiopsyCohortLung biopsyRespiratory failureLungEtiologyCohort studySurgeryIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Background. The aim of this study was to assess the utility of open lung biopsy in patients with hypoxic respiratory failure of unknown etiology admitted to an ICU and to examine the use of steroid therapy in this patient population. Methods. A retrospective cohort study was performed of all consecutive patients admitted to three tertiary care, university-affiliated, ICUs during the period from January 2000 to January 2012 with the principal diagnosis of hypoxic respiratory failure and who underwent an open lung biopsy. Results. Open lung biopsy resulted in a diagnostic yield of 68% and in a 67% change of management in patients. A multivariable analysis of clinical variables associated with acute hospital mortality demonstrated that postbiopsy systemic steroid therapy (OR 0.24, 95% C.I 0.06-0.96) was significantly associated with improved survival. Complications arising from the biopsy occurred in 30% of patients. Conclusion. Open lung biopsy had significant diagnostic yield and led to major changes in management and aided in end-of-life decision-making in the ICU. Systemic steroid therapy was associated with improved survival. The risk-benefit ratio of open lung biopsy is still unclear, especially given the availability of newer diagnostic tests and possible empirical therapy with steroids.

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)0.001
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 score0.065
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.256
Teacher spread0.246 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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