Surgeon Attitudes Toward Nonphysician Screening of Low Back or Low Back–Related Leg Pain Patients Referred for Surgical Assessment
Bibliographic record
Abstract
STUDY DESIGN: Questionnaire survey. OBJECTIVE: To explore spine surgeons' attitudes toward the involvement of nonphysician clinicians (NPCs) to screen patients with low back or low back-related leg pain referred for surgical assessment. SUMMARY OF BACKGROUND DATA: Although the utilization of physician assistants is common in several healthcare systems, the attitude of spine surgeons toward the independent assessment of patients by NPCs remains uncertain. METHODS: We administered a 28-item survey to all 101 surgeon members of the Canadian Spine Society, which inquired about demographic variables, patient screening efficiency, typical wait times for both assessment and surgery, important components of low back-related complaints history and examination, indicators for assessment by a surgeon, and attitudes toward the use of NPCs to screen patients with low back and leg pain referred for elective surgical assessment. RESULTS: Eighty-five spine surgeons completed our survey, for a response rate of 84.1%. Most respondents (77.6%) were interested in working with an NPC to screen patients with low back-related complaints referred for elective surgical assessment. Perception of suboptimal wait time for consultation and poor screening efficiency for surgical candidates were associated with greater surgeon interest in an NPC model of care. We achieved majority consensus regarding the core components for a low back-related complaints history and examination, and findings that would support surgical assessment. A majority of respondents (75.3%) agreed that they would be comfortable not assessing patients with low back-related complaints referred to their practice if indications for surgery were ruled out by an NPC. CONCLUSION: The majority of Canadian spine surgeons were open to an NPC model of care to assess and triage nonurgent or emergent low back-related complaints. Clinical trials to establish the effectiveness and acceptance of an NPC model of care by all stakeholders are urgently needed.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".