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Options in Prehospital analgesia

2002· review· en· W1977834567 on OpenAlexaff
Meredith L Borland, Ian Jacobs, Ian R. Rogers

Bibliographic record

VenueEmergency Medicine · 2002
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineNalbuphineMEDLINEIntensive care medicineTramadolRandomized controlled trialEmergency medicineAnalgesicAnesthesiaOpioidSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Prehospital analgesia options for paramedics have been limited due to the difficulty in achieving safe and effective pain relief without compromising transportation to hospital. The present paper identifies the analgesia methods currently available in the prehospital setting so as to evaluate the various options and highlight areas for future research. METHODS: A literature review of Medline and Embase databases from 1966 until the present was undertaken. Further hand searching of all the references identified in these papers was also performed. All current literature was analysed and categorized according to one of four levels of evidence using National Health and Medical Research Council of Australia guidelines (1999). RESULTS: There is a paucity of randomized control trials relating to prehospital analgesia. All published literature was level III or IV prospective or retrospective studies. Drug options used included nitrous oxide/oxygen mixtures, intravenous/intramuscular nalbuphine, intravenous tramadol and intravenous pure opiate agonists. CONCLUSIONS: The evidence supporting analgesic options in the prehospital setting is limited. There are few published data in this area despite the inadequacy of pain relief being recognized as a weakness in prehospital care. Prehospital analgesia is an area worthy of innovative methods for the administration of safe and effective analgesics without significant impact on transport times. Such methods should be prospectively evaluated in well-constructed trials.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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)0.0230.001

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 head0.089
GPT teacher head0.374
Teacher spread0.285 · 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 designNot applicable
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

Citations40
Published2002
Admission routes1
Has abstractyes

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