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Record W2430835012 · doi:10.1186/s13054-016-1377-3

Volumetric capnography: lessons from the past and current clinical applications

2016· review· en· W2430835012 on OpenAlexaff
Sara Verscheure, Paul Massion, Franck Verschuren, Pierre Damas, Sheldon Magder

Bibliographic record

VenueCritical Care · 2016
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCapnographyIntensive care medicineCurrent (fluid)Medical physicsEmergency medicineMedical emergencyAnesthesia

Abstract

fetched live from OpenAlex

Dead space is an important component of ventilation-perfusion abnormalities. Measurement of dead space has diagnostic, prognostic and therapeutic applications. In the intensive care unit (ICU) dead space measurement can be used to guide therapy for patients with acute respiratory distress syndrome (ARDS); in the emergency department it can guide thrombolytic therapy for pulmonary embolism; in peri-operative patients it can indicate the success of recruitment maneuvers. A newly available technique called volumetric capnography (Vcap) allows measurement of physiological and alveolar dead space on a regular basis at the bedside. We discuss the components of dead space, explain important differences between the Bohr and Enghoff approaches, discuss the clinical significance of arterial to end-tidal CO2 gradient and finally summarize potential clinical indications for Vcap measurements in the emergency room, operating room and ICU.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.204
GPT teacher head0.507
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations114
Published2016
Admission routes1
Has abstractyes

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