Preoperative Mental Health Component Scoring Is Related to Patient Reported Outcomes Following Lumbar Fusion
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort review. OBJECTIVE: The objective of this study was to identify depression using the Mental Component Score (MCS-12) of the Short Form-12 (SF-12) survey and to correlate with patient outcomes. SUMMARY OF BACKGROUND DATA: The impact of preexisting depressive symptoms on health-care related quality of life (HRQOL) outcomes following lumbar spine fusion is not well understood. METHODS: Patients undergoing lumbar fusion between one to three levels at a single center, academic hospital were retrospectively identified. Patients under the age of 18 years and those undergoing surgery for infection, trauma, tumor, or revision, and less than 1-year follow-up were excluded. Patients with depressive symptoms were identified using an existing clinical diagnosis or a score of MCS-12 less than or equal to 45.6 on the preoperative SF-12 survey. Absolute HRQOL scores, the recovery ratio (RR) and the percent of patients achieving minimum clinically important difference (MCID) between groups were compared, and a multiple linear regression analysis was performed. RESULTS: A total of 391 patients were included in the total cohort, with 123 (31.5%) patients reporting symptoms of depression based on MCS-12 and 268 (68.5%) without these symptoms. The low MCS-12 group was found to have significantly worse preoperative Oswestry disability index (ODI), visual analogue scale back pain (VAS Back) and visual analogue scale leg pain (VAS Leg) scores, and postoperative SF-12 physical component score (PCS-12), ODI, VAS Back, and VAS Leg pain scores (P < 0.05) than the non-depressed group. Finally, multiple linear regression analysis revealed preoperative depression to be a significant predictor of worse outcomes after lumbar fusion. CONCLUSION: Patients with depressive symptoms, identified with an MCS-12 cutoff below 45.6, were found to have significantly greater disability in a variety of HRQOL domains at baseline and postoperative measurement, and demonstrated less improvement in all outcome domains included in the analysis compared with patients without depression. However, while the improvement was less, even the low MCS-12 cohort demonstrated statistically significant improvement in all HRQOL outcome measures after surgery. LEVEL OF EVIDENCE: 3.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".