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Comparison of Clinical Performance of Cranial Computed Tomography Rules in Patients With Minor Head Injury: A Multicenter Prospective Study

2011· article· en· W2115545272 on OpenAlexaboutno aff
Young Sun Ro, Sang Do Shin, James F. Holmes, Kyoung Jun Song, Ju Ok Park, Jin Seong Cho, Seung Chul Lee, Seong Chun Kim, Ki Jeong Hong, Chang Bae Park, Eui Jung Lee, Yu Jin Kim, Ki Ok Ahn, Marcus Eng Hock Ong

Bibliographic record

VenueAcademic Emergency Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEmergency departmentProspective cohort studyObservational studyHead injuryTraumatic brain injuryEmergency medicineBluntConfidence intervalCohortPopulationComputed tomographyRadiologyInternal medicineSurgeryPsychiatry

Abstract

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OBJECTIVES: The objective was to compare the predictive performance of three previously derived cranial computed tomography (CT) rules, the Canadian CT Head Rule (CCHR), the New Orleans Criteria (NOC), and National Emergency X-Ray Utilization Study (NEXUS)-II, for detecting clinically important traumatic brain injury (TBI) and the need for neurosurgical intervention in patients with blunt head trauma. METHODS: This was a prospective, multicenter, observational cohort study of patients with blunt head trauma from June 2008 to May 2009. The historical and physical examination components of the CCHR, NOC, and NEXUS-II were documented on a data collection form and the performance of each of the three rules was compared. Patient eligibility for each specific rule was defined exactly as previously described for each specific rule. To compare the three decision rules in terms of sensitivity and specificity, an intersection cohort satisfying inclusion criteria of all three decision rules was derived. The primary outcome was clinically important TBI, and the secondary outcome was neurosurgical intervention. The sensitivity and specificity of each rule were calculated with 95% confidence intervals (95% CIs). We also calculated the potential reduction rate in cranial CT scan utilization realized by theoretical implementation of these rules. RESULTS: A total of 7,131 patients were prospectively enrolled, including 692 (9.7%) with clinical TBI. Among the enrolled population, patients eligible for CCHR, NOC, and NEXUS-II totaled 696, 677, and 2,951, respectively. The sensitivity and specificity for clinically important brain injury were as follows: CCHR, 112 of 144 (79.2%, 95% CI = 70.8% to 86.0%) and 228 of 552 (41.3%, 95% CI = 37.3% to 45.5%); NOC, 91 of 99 (91.9%, 95% CI = 84.7% to 96.5%) and 125 of 558 (22.4%, 95% CI = 19.0% to 26.1%); and NEXUS-II, 511 of 576 (88.7%, 95% CI = 85.8% to 91.2%) and 1,104 of 2,375 (46.5%, 95% CI = 44.5% to 48.5%). The sensitivity and specificity for neurosurgical intervention were as follows: CCHR, 100% (95% CI = 59.0% to 100.0%) and 38.3% (95% CI = 34.5% to 41.9%); NOC, 100% (95% CI = 54.1% to 100.0%) and 20.4% (95% CI = 17.4% to 23.7%); and NEXUS-II, 95.1% (95% CI = 90.1% to 98.0%) and 41.4% (95% CI = 39.5% to 43.2%). Among the enrolled population, intersection patients of CCHR, NOC, and NEXUS-II totaled 588. The sensitivity and specificity for clinically important brain injury were as follows: CCHR, 73 of 98 (74.5%, 95% CI = 64.7% to 82.8%) and 201 of 490 (41.0%, 95% CI = 36.6% to 45.5%); NOC, 89 of 98 (90.8%, 95% CI = 83.3% to 95.7%) and 112 of 490 (22.9%, 95% CI = 19.2% to 26.8%); and NEXUS-II, 82 of 98 (83.7%, 95% CI = 74.8% to 90.4%) and 172 of 490 (35.1%, 95% CI = 30.9% to 39.5%). The potential reduction in emergency CT scans by using these decision rules would have been higher with the NEXUS-II rule (39.6%, 95% CI = 37.8% to 41.4%) than with the CCHR rule (27.0%, 95% CI = 23.7% to 30.3%) or NOC rule (20.2%, 95% CI = 17.2% to 23.3%). CONCLUSIONS: For clinically important TBI, the three cranial CT decision rules had much lower sensitivities in this population than the original published studies, while the specificities were comparable to those studies. The sensitivities for neurosurgical intervention, however, were comparable to the original studies. The NEXUS-II rule showed the highest reduction rate for CT scans compared to other rules, but failed to identify all undergoing neurosurgical intervention for their original inclusion cohort.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.396
Teacher spread0.306 · 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 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".

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Citations87
Published2011
Admission routes1
Has abstractyes

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