Patient-reported outcomes in spine surgery: past, current, and future directions
Bibliographic record
Abstract
The purpose of this article is to review the current state of outcome measurement in spine surgery, with an emphasis on patient-reported outcome measures (PROMs). The commonly used generic and disease-specific outcome measures used in spinal surgery and research will be discussed. The authors will introduce the concepts of response shift and appraisal processes, which may affect the face validity of PROMs, as well as their interpretation over time. It is not uncommon for there to be a discrepancy between the observed and expected outcome, which is not wholly explainable by objective measures. Current work on understanding how appraisal affects outcome measurement will be discussed, and future directions will be suggested to facilitate the continued evolution of PROMs.There has been an evolution in the way clinicians measure outcomes following spinal surgery. In moving from purely physical, objective measures to a growing emphasis on the patient's perspective, spine surgery outcomes are better able to integrate the impact at multiple levels of relevant change. Appraisal concepts and methods are gaining traction as ways to understand the cognitive processes underlying PROMs over time. Measurement of appraisal is a valuable adjunct to the current spine outcome tools.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".