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Record W2075153095 · doi:10.1055/s-0033-1342973

Evaluation and Treatment of Pain in Critically Ill Adults

2013· review· en· W2075153095 on OpenAlexaff
Matt Hallman, Céline Gélinas, Daniel Herr, Kathleen Puntillo, Aaron M. Joffe

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineIntensive care medicineCritically illResource consumptionIntensive care unitOpiateIntensive care

Abstract

fetched live from OpenAlex

Pain is experienced by the overwhelming majority of patients during their intensive care unit stay, but it remains an underappreciated problem. To effectively treat pain, it must be detected and quantified using a validated assessment tool. It is acknowledged that optimal pain relief may be difficult to achieve given the complex interplay of coexisting medical conditions and the environment in which care is provided. Nonetheless, by following structured approaches to pain, resource consumption may be reduced, and even improved survival may be realized. This review covers practices and techniques specific to addressing and treating pain in the adult intensive care environment. Traditional pharmacological approaches including opiate and nonopiate medications are reviewed, as are regional anesthetic techniques and nonpharmacological approaches used for controlling 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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.083
GPT teacher head0.410
Teacher spread0.327 · 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.

Study designOther design
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

Citations34
Published2013
Admission routes1
Has abstractyes

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