Pain Intensity and Patientsʼ Acceptance of Surgical Complication Risks With Lumbar Fusion
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional study with prospective recruitment. OBJECTIVE: To determine the relationship of pain intensity (back and leg) on patients' acceptance of surgical complication risks when deciding whether or not to undergo lumbar spinal fusion. SUMMARY OF BACKGROUND DATA: To formulate informed decisions regarding lumbar fusion surgery, preoperative discussions should include a review of the risk of complications balanced with the likelihood of symptom relief. Pain intensity has the potential to influence a patient's decision to consent to lumbar fusion. We hypothesized that pain intensity is associated with a patient's acceptance of surgical complication risks. METHODS: Patients being seen for the first time by a spine surgeon for treatment of a nontraumatic or non-neoplastic spinal disorder completed a structured questionnaire. It posed 24 scenarios, each presenting a combination of risks of 3 complications (nerve damage, wound infection, and nonunion) and probabilities of symptom relief. For each scenario, the patient indicated whether he or she would or would not consent to a fusion for low back pain (LBP). The sum of the scenarios in which the patient responded that he or she would elect surgery was calculated to represent acceptance of surgical complication risks. A variety of other data were also recorded, including age, sex, education level, race, history of nonspinal surgery, duration of pain, and history of spinal injections. Data were analyzed using bivariate and multivariate regression analyses. RESULTS: The mean number of scenarios accepted by 118 enrolled subjects was 10.2 (median, 8; SD, 8.5; range, 0-24, or 42.5% of scenarios). In general, subjects were more likely to accept scenarios with lower risks and higher efficacy. Spearman rank correlation estimates demonstrated a moderate association between the LBP intensity and acceptance of surgical complication risks (r = 0.37, P = 0.0001) whereas leg pain intensity had a weak but positive correlation (r = 0.19, P = 0.04). In bivariate analyses, a history of spinal injections was strongly associated with patients' acceptance of surgical complication risks and willingness to proceed with surgery (54.5% of scenarios accepted for those who had a history of spinal injections vs. 27.6% for those with no history of spinal injections, P = 0.0001). White patients were more willing to accept surgery (45.9% of scenarios) than non-white patients (28.4%, P = 0.03). With the available numbers, age, sex, history of nonspinal surgery, education, and the duration of pain demonstrated no clear association with acceptance of surgical complication risks. Although education overall was not an influential factor, more educated males had greater risk tolerance than less educated males whereas more educated females had less risk tolerance than less educated females (P = 0.023). In multivariate analysis, LBP intensity remained a highly statistically significant correlate (P = 0.001) of the proportion of scenarios accepted, as did a history of spinal injections (P = 0.001) and being white (0.03). CONCLUSION: The current investigation indicates that the intensity of LBP is the most influential factor affecting a patient's decision to accept risk of complication and symptom persistence when considering lumbar fusion. This relationship has not been previously shown for any surgical procedure. These data could potentially change the manner in which patients are counseled to make informed choices about spinal surgery. With growing interest in adverse events and complications, these data could be important in establishing guidelines for patient-directed surgical decision making.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".