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Record W2078661770 · doi:10.4103/0972-5229.25919

Acute effects of nitric oxide inhalation in ARDS: A dose finding study at steady state kinetics

2006· article· en· W2078661770 on OpenAlexaff
Anjan Trikha, Ritu Madan, H. L. Kaul

Bibliographic record

VenueIndian Journal of Critical Care Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineARDSInhalationNitric oxideKineticsSteady state (chemistry)AnesthesiaInternal medicineLung

Abstract

fetched live from OpenAlex

Background: Inhaled Nitric oxide (INO) decreases pulmonary artery pressures and improves oxygenation in patients with ARDS. Aim: To evaluate the dose response to 1-20 parts per million (ppm) INO in ARDS, by noting changes in oxygenation, pulmonary artery systolic pressures (PASP) and to determine optimum dose. Methodology and Design: Prospective study. Setting: 10 bed general intensive care unit. Patients: 13 consecutive patients with ARDS. Interventions: INO was given between 1-20 ppm with 15 minutes at each concentration via an insufflator from a high pressure source, to the inspiratory limb of the ventilator. Study had ascending and descending phase. Results and Conclusions: The optimum dose of INO to improve oxygenation was between 3 and 10 ppm. PaO 2 improvement was independent of pulmonary haemodynamic changes. The pulmonary haemodynamic changes needed higher INO initially. Once stabilized, INO could be brought down to concentrations at which maximum improvement in PaO occurred. 2 The 'responders' had lesser duration of pre INO ventilation and lower PaO 2 /FiO 2 . Key words: ARDS, nitric oxide, acute effects Adult respiratory distress syndrome (ARDS) is oxide [INO] therapy has not been shown to improve the characterized by acute respiratory distress, refractory outcome and the optimum dose of INO in ARDS is not hypoxaemia and pulmonary hypertension and remains defined. a challenging organ failure for the intensivist. Accumulating data from basic science and clinical studies

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.216
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.322
Teacher spread0.306 · 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 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

Citations1
Published2006
Admission routes1
Has abstractyes

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