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Record W1985653435 · doi:10.1503/cjs.003713

Spine surgeons’ requirements for imaging at the time of referral: a survey of Canadian spine surgeons

2014· article· en· W1985653435 on OpenAlexafffundvenueabout
Jason W. Busse, John J. Riva, Raja Rampersaud, Michael Goytan, Thomas E. Feasby, Martin H. Reed, John J. You

Bibliographic record

VenueCanadian Journal of Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of CalgaryChildren's Hospital of WinnipegUniversity of ManitobaUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineMagnetic resonance imagingSPINE (molecular biology)ReferralRadiographyMedical imagingRadiologyMedical physicsPhysical therapyFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Routine imaging of patients with spine-related complaints referred for surgical assessment may represent an inefficient use of technological resources. Our objective was to explore Canadian spine surgeons' requirements with respect to imaging studies accompanying spine-related referrals. METHODS: We administered an 8-item survey to all 100 actively practising surgeon members of the Canadian Spine Society that inquired about demographic variables and imaging requirements for patients referred with spine-related complaints. RESULTS: Fifty-five spine surgeons completed our survey, for a response rate of 55%. Most respondents (43; 78%) required imaging studies to accompany all spine-related referrals. The type of imaging required was highly variable, with respondents endorsing 7 different combinations. Half (47%) required magnetic resonance imaging and 38% required plain radiographs either alone or in combination with other forms of imaging. Half of the respondents refused to see 20% or more of all patients referred for spine-related complaints. CONCLUSION: Most Canadian spine surgeons require imaging studies to accompany spine-related referrals; however, the type and combination of studies is highly variable, and many patients who are referred are never seen (for a consultation). Standardization and optimization of imaging practices for patients with spine-related complaints referred for surgical assessment may be an important area for cost savings.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.096
GPT teacher head0.303
Teacher spread0.207 · 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 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".

Quick stats

Citations11
Published2014
Admission routes4
Has abstractyes

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