Patients’ Attitudes Toward Nonphysician Screening of Low Back and Low Back Related Leg Pain Complaints Referred for Surgical Assessment
Bibliographic record
Abstract
STUDY DESIGN: A questionnaire survey. OBJECTIVE: The aim of this study was to explore patient attitudes toward screening to assess suitability for low back surgery by nonphysician health care providers. SUMMARY OF BACKGROUND DATA: Canadian spine surgeons have shown support for nonphysician screening to assess and triage patients with low back pain and low back related leg pain. However, patients' attitudes toward this proposed model are largely unknown. METHODS: We administered a 19-item cross-sectional survey to adults with low back and/or low back related leg pain who were referred for elective surgical assessment at one of five spine surgeons' clinics in Hamilton, Ontario, Canada. The survey inquired about demographics, expectations regarding wait time for surgical consultation, as well as willingness to pay, travel, and be screened by nonphysician health care providers. RESULTS: Eighty low back patients completed our survey, for a response rate of 86.0% (80 of 93). Most respondents (72.5%; 58 of 80) expected to be seen by a surgeon within 3 months of referral, and 88.8% (71 of 80) indicated willingness to undergo screening with a nonphysician health care provider to establish whether they were potentially a surgical candidate. Half of respondents (40 of 80) were willing to travel >50 km for assessment by a nonphysician health care provider, and 46.2% were willing to pay out-of-pocket (25.6% were unsure). However, most respondents (70.0%; 56 of 80) would still want to see a surgeon if they were ruled out as a surgical candidate, and written comments from respondents revealed concern regarding agreement between surgeons' and nonphysicians' determination of surgical candidates. CONCLUSION: Patients referred for surgical consultation for low back or low back related leg pain are largely willing to accept screening by nonphysician health care providers. Future research should explore the concordance of screening results between surgeon and nonphysician health care providers. LEVEL OF EVIDENCE: 3.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".