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Record W4245125250 · doi:10.4187/respcare.07425

Esophageal Manometry

2020· review· en· W4245125250 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRespiratory Care · 2020
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSinai Health SystemUniversity Health NetworkUniversity of TorontoSt. Michael's Hospital
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineIntensive care medicineMechanical ventilationClinical PracticePhysical therapyAnesthesia

Abstract

fetched live from OpenAlex

The estimation of pleural pressure with esophageal manometry has been used for decades, and it has been a fertile area of physiology research in healthy subject as well as during mechanical ventilation in patients with lung injury. However, its scarce adoption in clinical practice takes its roots from the (false) ideas that it requires expertise with years of training, that the values obtained are not reliable due to technical challenges or discrepant methods of calculation, and that measurement of esophageal pressure has not proved to benefit patient outcomes. Despites these criticisms, esophageal manometry could contribute to better monitoring, optimization, and personalization of mechanical ventilation from the acute initial phase to the weaning period. This review aims to provide a comprehensive but comprehensible guide addressing the technical aspects of esophageal catheter use, its application in different clinical situations and conditions, and an update on the state of the art with recent studies on this topic and on remaining questions and ways for improvement.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.073
GPT teacher head0.371
Teacher spread0.298 · 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