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Record W3127779781 · doi:10.1016/j.bjane.2021.02.002

A survey of acute pain service in Canadian teaching hospitals

2021· article· en· W3127779781 on OpenAlexaffabout
Qutaiba A. Tawfic, Alexander Freytag, Kevin Armstrong

Bibliographic record

VenueBrazilian Journal of Anesthesiology (English Edition) · 2021
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineAmbulatoryAcute painFamily medicinePain managementAnesthesiaEmergency medicineMedical emergencySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The first national survey to ascertain the prevalence, structure, and functioning of the APS in Canadian university affiliated hospitals was conducted in 1991. This is a follow-up survey to assess the current status of the APS in Canada. METHODS: We requested completion of a 26-question survey from lead personnel of the APS teams or Anesthesia departments of Canadian teaching hospitals. RESULTS: Among the 32 centers that were contacted, 21 (65.6%) responded. Of these respondents, 18 (85.7%) indicated that they have a structured APS (72.22% adults, 5.56% pediatrics, 22.22% mixed). Among the 18 centers with an APS, 16 of the services are led by an anesthesiologist. Eight centers (44.44%) have a regional anesthesia group, of which five (27.75%) have a regional anesthesia group that is distinct from the APS team. Nine centers (50%) offer ambulatory nerve catheter analgesia after discharge home. Fifteen centers (83.33%) use standardized order sets, and 13 centers (72.22%) use an electronic record for APS. More than 50% of the centers use intravenous lidocaine and ketamine as a part of their multimodal analgesia. CONCLUSION: Most Canadian teaching hospitals do have a functioning APS. This survey has the potential to generate research questions about the availability of standardized and advanced acute pain management in Canada's teaching hospitals.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.030
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueBrazilian Journal of Anesthesiology (English Edition)Same topicPediatric Pain Management TechniquesFrench-language works237,207