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Canadian C-spine Audit: Appropriateness of C-spine Radiograph Requests in Adult Spinal Injury Assessment ID 10371

2019· preprint· en· W4251291118 on OpenAlexaboutno aff
Ravi Rait, Inderjeet Nagra, Ross Hodson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAuditRadiographyChecklistRadiological weaponNiceRadiology

Abstract

fetched live from OpenAlex

Aims and Objectives: To establish whether a newly instituted head and neck care bundle (a checklist document), following recommendations by the initial audit appropriateness of C-spine Radiograph Requests in Spinal Injury or direct CT C-spine in accordance to NICE guidance NG41 showed an improvement in the use of the correct first time modality (X-ray or CT). Methodology: This was a retrospective study using the CRIS system to evaluate all patients that attended accident and emergency in the months of January and February 2019. Thirty-five patients had C-spine radiographs and 127 patients had C-spine CT scans. The patient were categorised into high and low risk based on NICE guidance NG41 and the use of patient notes via Patient First. Results: Data was collected and compared with the first audit (ID1614). 74.4% of patients received the appropriate first line radiological modality, compared with 90.7% of patients after the newly instituted head and neck bundle. Discussion: High risk patients were supposed to have a CT first line while low risk patients were supposed to have plain films as a first line modality. The increase in percentage of patients receiving the correct first line modality was thought to be as a result of the newly instituted head and neck care bundle. Whilst this is an improvement, there were still 15 patients who were at high risk of c-spine fracture who did not receive the appropriate first line modality (CT) and therefore could have been missed.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.260
Teacher spread0.253 · 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 designSimulation or modeling
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

Citations0
Published2019
Admission routes1
Has abstractyes

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