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Record W4220686308 · doi:10.1136/thoraxjnl-2021-217578

Past, present and future of conservative oxygen therapy in critical care

2022· article· en· W4220686308 on OpenAlexaff
Daniel Martín, David A Harrison, Paul Mouncey, B. Ronan O’Driscoll, Lorna Miller, Doug W Gould, Alvin Richards‐Belle, Kathy Rowan

Bibliographic record

VenueThorax · 2022
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsRoyal University Hospital
FundersNational Institute for Health and Care Research
KeywordsMedicineOxygen therapyIntensive care medicineCritical illnessInternal medicineCritically ill

Abstract

fetched live from OpenAlex

Conservative oxygen therapy (COT) is the administration lower levels of supplemental\noxygen than usual in order to tolerate a lower level of arterial oxygenation (either the partial\npressure (PaO2) or haemoglobin saturation (SaO2)) than normal. Its purpose is to reduce a\npatient’s overall exposure to additional oxygen in order to minimise the risk of oxyen\ntoxicity.1 This approach to oxygen therapy has also been called permissive hypoxaemia\n(PH) and the terms are frequently used interchangeably; here, we refer to all efforts to\nreduce supplemental oxygen administration or arterial oxygenation as COT. Studies have\nbeen conducted across a wide range of medical conditions, to determine whether COT\nimproves patient outcomes and there appears to be a signal of benefit among acutely unwell\npatients.2 The intention in this article, however, is to focus only on critically ill patients\nadmitted to intensive care units (ICUs). These patients often present with acute hypoxaemic\nrespiratory failure and require high concentration oxygen to restore normal arterial\noxygenation. There is concern thatone of the central pillars of support for these patients,\noxygen, may inadvertently be causing them harm, which we mistakenly ascribe to a\nworsening of their underlying pathology. There remains no consensus on how or when to\nuse COT in critically ill patients and it is imperative we address these questions as soon as\npossible.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.549

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)0.0010.000

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 head0.048
GPT teacher head0.352
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
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

Citations3
Published2022
Admission routes1
Has abstractyes

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