Patient Predictors of Surgical Candidacy in Elective Spine Disorders
Bibliographic record
Abstract
BACKGROUND: The expansion of age-related degenerative spine pathologies has led to increased referrals to spine surgeons. However, the majority of patients referred for surgical consultation do not need surgery, leading to inefficient use of healthcare resources. This study aims to elucidate preoperative patient variables that are predictive of patients being offered spine surgery. METHODS: We conducted an observational cohort study on patients referred to our institution between May 2013 and January 2015. Patients completed a detailed preclinic questionnaire on items such as history of presenting illness, quality-of-life questionnaires, and past medical history. The primary end point was whether surgery was offered. A multivariable logistical regression using the random forest method was used to determine the odds of being offered surgery based on preoperative patient variables. RESULTS: An analysis of 1194 patients found that preoperative patient variables that reduced the odds of surgery being offered include mild pain (odds ratio [OR] 0.37, p=0.008), normal walking distance (OR 0.51, p=0.007), and normal sitting tolerance (OR 0.58, p=0.01). Factors that increased the odds of surgery include radiculopathy (OR 2.0, p=0.001), patient's belief that they should have surgery (OR 1.9, p=0.003), walking distance <50 ft (OR 1.9, p=0.01), relief of symptoms when bending forward (OR 1.7, p=0.008) and sitting (OR 1.6, p=0.009), works more slowly (OR 1.6 p=0.01), aggravation of symptoms by Valsalva (OR 1.4, p=0.03), and pain affecting sitting/standing (OR 1.1, p=0.001). CONCLUSIONS: We identified 11 preoperative variables that were predictive of whether patients were offered surgery, which are important factors to consider when screening outpatient spine referrals.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".