Functional Status Correlates of Change and Stability in Appraisal after Spine Surgery: Earlier versus Later Effects
Bibliographic record
Abstract
Spine surgery generally yields a notable improvement in patients' health state, and there is variability in measured patient outcomes after spine surgery. The present work aimed to describe for clinicians how appraisal underlies their patients' experience of healthcare interventions. This prospective longitudinal cohort study (n = 156) included adults undergoing spine surgery for degenerative spinal conditions. The analysis was a descriptive illustration of the relationship between change in the spine-related disability using the Oswestry Disability Index and change in cognitive-appraisal processes using the Quality-of-Life Appraisal Profilev2-Short Form, early versus later during the recovery trajectory (i.e., between baseline and 3 months post-surgery; and between 3 and 12 months post-surgery). Cognitive-appraisal processes related to Sampling of Experience showed greater change soon after surgery, whereas Standards of Comparison appraisals changed more later in the recovery trajectory. Different appraisal processes were emphasized by patients who reported worsening of the spine-related disability, as compared to those who reported no change or improvement. These findings suggest that changes in appraisal differ depending on the individual's experience of the impact of spine surgery. Appraisal processes thus reflect an ongoing dynamic in adaptation to changing function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".