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Record W7023955946

Prävalenz der nicht-invasiven Beatmung in deutschen Notaufnahmen

2023· dissertation· de· W7023955946 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedissertation
Languagede
FieldPhysics and Astronomy
TopicQuantum Chromodynamics and Particle Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsExacerbationGermanQuarter (Canadian coin)SedationEmergency departmentPneumoniaCOPD
DOInot available

Abstract

fetched live from OpenAlex

Background and objectives: Non-invasive Ventilation (NIV) is one of the main treatment methods of acute respir-atory insufficiency. So far there is no prevalence data regarding the provision of this kind of ventilation in Germany and in German emergency departments. This thesis focuses on obtaining an overview of the prevalence of non-invasive ventilation in German emergency departments. Materials: A list of emergency departments was created for this purpose and nearly a quarter of all German emergency departments were interviewed with an online questionnaire during September and October of 2017. The questionnaire contained 62 items, struc-tured into 5 categories: Organizational matters, NIV-availability, application, NIV on patients with AECOPD (acute exacerbation of chronic obstructive pulmonary dis-ease), NIV on patients with ACPE (acute cardiogenic pulmonary edema). Results: The response rate was 125 out of 276 emergency departments (nearly 50%) treating about 5 million patients a year. As a result over 80% of the participating hospitals are able to offer NIV in their emergency departments. More than a half of them established NIV in the last five years. In average 120 NIV-applications are performed in each emergency department yearly. The two main indications for NIV are AECOPD (acute exacerbation of chronic ob-structive pulmonary disease) and ACPE (acute cardiogenic pulmonary edema). In third place the community acquired pneumonia is mentioned. The respirator-settings usually are the physicians duty, but nearly in a quarter of all situations the nursing staff is instructed to take care of this task. Sedation is applied by nearly 80% of the participants as supportive medication. Morphin is most commonly used while Midazolam was selected as a second choice. Sedation based on specific diagnoses only plays a minor role. Only while using Ketanest as a sedative for patients with AECOPD and Fentanyl as a sedative for pa-tients with ACPE, significant differences appear. A bronchodilatation mostly is achieved by means of Salbutamol. The acceptance of NIV is highest among the group of leading physicians such as the medical superintendents or the heads of the emergency departments. House officers on duty hesitantly accept this form of therapy. Quality assurance measures are not implemented in nearly 40% of the participating hospitals, clinical standard operation procedures for applying NIV are only available in less than half of the emergency departments. Conclusion: In spite of a current NIV-proportion of 40% in German emergency departments there are no clinical studies for this special setting. The heterogeneous point of view of the participants concerning indications and contraindications of NIV clarifies this state-ment. Furthermore it is necessary to establish external quality assurance programs with a higher measure of standardization. There is less heterogeneity in sedation of NIV-patients, however other recommenda-tions in contrast to the daily practice can be observed.

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)0.002
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.304
Teacher spread0.286 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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