Performance Indicators in Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVE: To elucidate how performance indicators are currently used in spine surgery. SUMMARY OF BACKGROUND DATA: The Patient Protection and Affordable Care Act has given significant traction to the idea that healthcare must provide value to the patient through the introduction of hospital value-based purchasing. The key to implementing this new paradigm is to measure this value notably through performance indicators. METHODS: MEDLINE, CINAHL Plus, EMBASE, and Google Scholar were searched for studies reporting the use of performance indicators specific to spine surgery. We followed the Prisma-P methodology for a systematic review for entries from January 1980 to July 2016. All full text articles were then reviewed to identify any measure of performance published within the article. This measure was then examined as per the three criteria of established standard, exclusion/risk adjustment, and benchmarking to determine if it constituted a performance indicator. RESULTS: The initial search yielded 85 results among which two relevant studies were identified. The extended search gave a total of 865 citations across databases among which 15 new articles were identified. The grey literature search provided five additional reports which in turn led to six additional articles. A total of 27 full text articles and reports were retrieved and reviewed. We were unable to identify performance indicators. The articles presenting a measure of performance were organized based on how many criteria they lacked. We further examined the next steps to be taken to craft the first performance indicator in spine surgery. CONCLUSION: The science of performance measurement applied to spine surgery is still in its infancy. Current outcome metrics used in clinical settings require refinement to become performance indicators. Current registry work is providing the necessary foundation, but requires benchmarking to truly measure performance. LEVEL OF EVIDENCE: 1.
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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 | 0.072 | 0.223 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".