Spine surgeons’ requirements for imaging at the time of referral: a survey of Canadian spine surgeons
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".