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Record W3111145903 · doi:10.2147/jpr.s279071

<p>Injection of Bupivacaine into the Pleural and Mediastinal Drains: A Novel Approach for Decreasing Incident Pain After Cardiac Surgery – Montreal Heart Institute Experience</p>

2020· article· en· W3111145903 on OpenAlexafffundabout
Jennifer Cogan, M André, Gabrielle Ariano-Lortie, Anna Nozza, Meggie Raymond, Antoine Rochon, Grisell Vargas-Shaffer

Bibliographic record

VenueJournal of Pain Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalMontreal Heart Institute
FundersInstitut de Cardiologie de Montréal
KeywordsMedicineBupivacaineAnesthesiaMediastinitisSurgeryIncidence (geometry)

Abstract

fetched live from OpenAlex

BACKGROUND: We conducted a chart review of prospectively collected data in order to demonstrate the safety and efficacy of an innovative technique of pleural and mediastinal drain injections. METHODS: Patients who had undergone cardiac surgery and who continued to have pain despite the use of a multimodal pain protocol received injections of 20 mL of 0.25% bupivacaine in pleural and/or mediastinal chest drainage tubes. RESULTS: Patients were evaluated for the incidence mediastinitis, osteitis, and deep sternal wound infection as well as the speed and intensity of pain relief. The odds ratio of infection in the infused group was 0.955 (CI = 0.4705, 1.9384). The adjusted mean "decrease in pain" was 4.01 (SEM = 0.15 and 95% CI = 3.78, 4.38), using the 11-point Likert Numerical Rating Scale. The mean adjusted "time to maximum pain relief" was 8.33 minutes (SEM = 0.42 and 95% CI = 7.50, 9.15). CONCLUSION: This technique is a powerful, safe, and efficient tool in the armamentarium of pain management and its growing use within our institution has provided a substantial benefit in the treatment of early post-operative pain.

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)0.022
metaresearch head score (Gemma)0.007
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 score0.098
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.077
GPT teacher head0.336
Teacher spread0.259 · 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

Citations8
Published2020
Admission routes3
Has abstractyes

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