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Record W4317884362 · doi:10.12968/hmed.2022.0420

Non-invasive support for the hypoxaemic patient

2023· review· en· W4317884362 on OpenAlexaff
S. Keshavjee, Naheed Jivraj, Ambika Tejpal, Michael C. Sklar

Bibliographic record

VenueBritish Journal of Hospital Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineIntensive care medicineVentilation (architecture)Coronavirus disease 2019 (COVID-19)OxygenationTriagePandemicMechanical ventilationAcute respiratory failureEmergency medicineAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Optimisation of oxygenation strategies in patients with hypoxaemic respiratory failure is a top priority for acute care physicians, as hypoxaemic respiratory failure is one of the leading causes of admission. Various oxygenation methods range from non-invasive face masks to high flow nasal cannulae, which have advantages and disadvantages for this heterogeneous patient group. Focus has turned toward examining the benefits of non-invasive ventilation, as this was heavily researched in resource-limited settings during the COVID-19 pandemic. The oxygenation strategy should be determined on an individualised basis for patients, and with new evidence from the COVID-19 pandemic, providers may now consider placing further emphasis on non-invasive approaches. As non-invasive ventilation continues to be used in increasing frequency, new methods of monitoring patient response, including when to escalate ventilation strategy, will need to be validated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.047
GPT teacher head0.330
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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