PATIENT PREFERENCES, REFERRAL PRACTICES, AND SURGEON ENTHUSIASM FOR DEGENERATIVE LUMBAR SPINAL SURGERY
Bibliographic record
Abstract
Degenerative disease of the lumbar spine (DDLS) is a common condition for which surgery is beneficial in selected patients. Wide variation in surgical referral and rates of surgery has been observed contributing to unequal access to care. Our objectives were to examine (1) the variation in preferences for referral and surgery among surgeons, family physicians (FPs) and patients, (2) how FP referral practices compare with preferences and guideline recommendations, and (3) how the ‘enthusiasm’ of patients and physicians influence regional variation in surgical rates. We used conjoint analysis in a mailed survey to elicit preferences based on clinical vignettes from surgeons, FPs and patients. A Delphi expert panel provided consensus guideline recommendations for surgical referral to compare with actual FP referral practices. Rates of surgery for DDLS, obtained from Ontario hospital discharge data, were used to quantify regional variation and regression models assessed the relationship with patient and physician enthusiasm. We identified significant differences in preferences for referral and surgery between patients, FPs and surgeons. Surgeons placed high importance on leg-dominant symptoms while patients had high importance for quality of life symptoms (i.e. severity, duration, walking tolerance). Surgical referral practices were poorly predicted by individual FP preferences and guideline recommendations based on clinical factors alone. Variation in Ontario surgical rates was higher than that of hip or knee replacements and was highly associated with the enthusiasm of surgeons (p<0.008), rather than FPs or patients. By appreciating the variation in preferences between patients and physicians, and exploring other non-clinical factors that influence referrals, we may be able to improve the efficiency of referrals and enhance the shared decision making process. With an understanding of the influence that surgeons have in driving variation in surgical rates, further research may allow us to direct strategies to improve access and allow for a more equitable delivery of care for patients suffering from DDLS.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".