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Record W2762185907 · doi:10.7759/cureus.1760

Determinants of Computed Tomography Head Scan Ordering for Patients with Low-Risk Headache in the Emergency Department

2017· article· en· W2762185907 on OpenAlexaffabout
Meaghan Mackenzie, Rashi Hiranandani, Dongmei Wang, Tak Fung, Eddy Lang

Bibliographic record

VenueCureus · 2017
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsAlberta Health ServicesUniversity of CalgaryUniversity of Ottawa
Fundersnot available
KeywordsMedicineEmergency departmentHeadachesSpecialtyLumbar punctureComputed tomographyNeurosurgeryNeuroradiologyEveningNeurologyEmergency medicinePediatricsRadiologyFamily medicineSurgeryInternal medicineCerebrospinal fluidPsychiatry

Abstract

fetched live from OpenAlex

Background Many specialty societies have found that neuroimaging in headache is a low-value intervention for benign presentations. This study describes factors that influence Emergency Room (ER) physicians' adherence to Choosing Wisely (CW) recommendations for low-risk headache patients presenting to Calgary's Emergency Departments (EDs). Emergency medicine has yet to address imaging in headache as a CW topic; however, this study may inform that decision. Methods Data were retrospectively collected for all patients presenting to Calgary EDs with headaches from April 1, 2014 to March 31, 2016. Patients were deemed low-risk by virtue of discharge home from the ED, age < 50, and no lumbar puncture (LP), trauma, neurology, or neurosurgery consult or red flags on history. The primary outcome was computed tomography (CT) ordering rates with an eye to medical doctor (MD) practice variation. Patient, physician, and environmental factors were analyzed to compare patients who did and did not receive a CT. Results Two thousand seven hundred and thirty-four headache patients met the eligibility criteria. A total of 117 Calgary ER physicians were included, all of whom had seen 10 or more headache patients over the study period. Physician practice variation was vast, with a mean ordering rate of 38.0% and a range of 0% to 95% (M = 39.0%, IQR = 21.0%). CTs were ordered more often in males than females (39.9%; 34.1%; p = 0.002) and in patients presenting during the day and evening (38.1%; 39.0%) compared to the night (29.7%; p < 0.001). Patients were divided into quartiles by age, with the oldest group (41.6 - 50 years) receiving significantly more head CTs (45.1%) than the other quartiles (34.9%; 34.9%; 27.5%; p < 0.001). Longer triage-to-discharge times were associated with an increase in CT ordering rates (12% for < 2.95 hours; 35% for > 4 hour wait; p < 0.001). Lastly, patients who did not have a CT were more likely to revisit the ED within seven days compared to those who did (6.9% vs 4.0%; p = 0.003), but their seven-day admission rate was unaffected (0.6% in the group that got CTs and 0.3% in the group that did not get a CT). Time to assessment, the day of the week, physician gender, years of experience, and training program did not influence CT ordering practices. Conclusion To our knowledge, this is the first study to assess how patient, physician, and environmental factors relate to the use of CT scans in low-risk headaches presenting to the ED. CW guidelines are not optimally adhered to, and the findings in this study findings may inspire new ideas for maximizing the judicious use of healthcare resources.

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)0.007
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.304
Teacher spread0.287 · 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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Citations11
Published2017
Admission routes2
Has abstractyes

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