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Record W4317902634 · doi:10.14740/jocmr4841

Improving the Emergency Department Management of Sickle Cell Vaso-Occlusive Pain Crisis: The Role and Options of Sublingual and Intranasally Administered Analgesia

2023· review· en· W4317902634 on OpenAlexvenueno aff
Ademola S. Ojo, Olumayowa G. Odipe, Oluwanifemi Owoseni

Bibliographic record

VenueJournal of Clinical Medicine Research · 2023
Typereview
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNasal administrationVaso-occlusive crisisEmergency departmentAcute painAnesthesiaSublingual administrationBreakthrough PainPopulationIntensive care medicineDiseaseSickle cell anemiaInternal medicineOpioidPharmacology

Abstract

fetched live from OpenAlex

Vaso-occlusive crisis (VOC), characterized by periods of excruciating pain is the most common clinical manifestation of sickle cell disease (SCD), often resulting in emergency room presentation. These patients often experience long wait times in the emergency department before receiving their first dose of analgesia. This delay results from the complexities of the emergency care system. Using the intranasal or sublingual approach to administering analgesia to SCD patients with VOC offers a fast, safe, noninvasive, atraumatic, and easily accessible route of administration which could reduce the time to first dose of analgesia. With the evolving advances in the development and delivery of analgesic medications, providers should be conversant with the nuances of intranasal and sublingual analgesia in the management of acute vaso-occlusive pain crisis. This review explores the pharmacokinetic profiles, dosages, and administration of intranasal and sublingual analgesics with relevance to the SCD population.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.162
GPT teacher head0.501
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of Clinical Medicine ResearchSame topicHemoglobinopathies and Related DisordersFrench-language works237,207