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Effect of repeated administration of low‐dose silver nitrate for pleurodesis in a rabbit model

2011· article· en· W1559982101 on OpenAlexaff
Alain Tremblay, David R. Stather, Margaret M. Kelly

Bibliographic record

VenueRespirology · 2011
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsUniversity of CalgaryInstitute of Infection and Immunity
Fundersnot available
KeywordsPleurodesisMedicineBolus (digestion)AnesthesiaSalineRegimenToxicitySurgeryInternal medicinePleural effusion

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: Both the efficacy and toxicity of sclerosing agents are likely to be dose-dependent. Clinical pleurodesis strategies typically involve single bolus dose administration of drugs. This study was designed to test whether repeated administration of low doses of silver nitrate (SN) could lead to effective pleurodesis. METHODS: Intrapleural administration, to rabbits, of decreasing doses of SN or normal saline was undertaken daily over 1, 5 or 14days. Assessment of the degree of pleurodesis was by visual inspection (score 1-8) and histological examination and scoring of inflammation and fibrosis (score 0-4). The untreated contralateral side was used as a control. A visual pleurodesis score of ≥5 was considered to be positive. RESULTS: The lowest concentrations of SN leading to a visual pleurodesis score ≥5 were 0.425%, 0.085% and 0.05% for 1, 5 and 14day administration protocols respectively (P<0.05 vs control side). Visual pleurodesis scores decreased as the dose of SN decreased within each administration regimen groups (P<0.05 for single and 14day groups, P=0.058 in 5day group). A significant correlation was noted between visual pleurodesis scores and histology fibrosis scores. CONCLUSIONS: Effective pleurodesis can be achieved in an animal model with repeated daily administration of SN at doses significantly lower than the lowest effective single day dose. This finding could lead to better tolerated pleurodesis regimens.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.290

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)0.0000.000

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 head0.036
GPT teacher head0.302
Teacher spread0.265 · 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 designBench or experimental
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

Citations16
Published2011
Admission routes1
Has abstractyes

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