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Record W4324017896 · doi:10.1017/s1049023x23000298

Efficacy of Fascia Iliaca Compartment Blocks in Proximal Femoral Fractures in the Prehospital Setting: A Systematic Review and Meta-Analysis

2023· review· en· W4324017896 on OpenAlexaff
Sabrina Slade, Evan Hanna, Josh Pohlkamp-Hartt, David W. Savage, Robert Ohle

Bibliographic record

VenuePrehospital and Disaster Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsAir CanadaNOSM University
Fundersnot available
KeywordsMedicineObservational studyMEDLINERandomized controlled trialCochrane LibraryAdverse effectSystematic reviewEmergency medicineAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction: Proximal femoral fractures are characterized as one of the most common and most painful injuries sustained by patients of all ages and are associated with high rates of oligoanalgesia in the prehospital setting. Current treatments include oral and parenteral opiates and sedative agents, however regional anesthesia techniques for pain relief may provide superior analgesia with lower risk of side effects during patient transportation. The fascia iliaca compartment block (FICB) is an inexpensive treatment which is performed with minimal additional equipment, ultimately making it suitable in prehospital settings. Problem: In adult patients sustaining proximal femoral fractures in the prehospital setting, what is the effect of the FICB on non-verbal pain scores (NVPS), patient satisfaction, success rate, and adverse events compared to traditional analgesic techniques? Methods: A librarian-assisted literature search was conducted of the Cochrane Database, Ovid MEDLINE, PubMed, Ovid EMBASE, Scopus, and Web of Science indexes. Additionally, reference lists for potential review articles from the British Journal of Anesthesia, the College of Anesthetists of Ireland, the Journal of Prehospital Emergency Care, Annales Francaises d’Anesthesie et Réanimation, and the Scandinavian Journal of Trauma, Resuscitation, and Emergency Medicine were reviewed. Databases and journals were searched during the period from January 1, 1980 through July 1, 2022. Each study was scrutinized for quality and validity and was assigned a level of evidence as per Oxford Center for Evidence-Based Medicine guidelines. Results: Five studies involving 340 patients were included (ie, two randomized control trials [RCTs], two observational studies, and one prospective observational study). Pain scores decreased after prehospital FICB across all included studies by a mean of 6.65 points (5.25 - 7.5) on the NVPS. Out of the total 257 FICBs conducted, there was a success rate of 230 (89.3%). Of these, only two serious adverse events were recorded, both of which related to local analgesia toxicity. Neither resulted in long-term sequelae and only one required treatment. Conclusion: Use of FICBs results in a significant decrease in NVPS in the prehospital setting, and they are ultimately suitable as regional analgesic techniques for proximal femur fractures. It carries a low risk of adverse events and may be performed by health care practitioners of various backgrounds with suitable training. The results suggest that FICBs are more effective for pain management than parenteral or oral opiates and sedative agents alone and can be used as an appropriate adjunct to pain management.

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)0.011
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.027
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.370
Teacher spread0.292 · 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 designMeta-analysis
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

Citations18
Published2023
Admission routes1
Has abstractyes

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