Validating a therapy-oriented complication grading system in lumbar spine surgery: a prospective population-based study
Bibliographic record
Abstract
The aim of the present study was to validate a therapy-oriented complication grading system in a well-defined neurosurgical patient population in which complications may entrain neurological deficits, which are severe but not treated. The prospective patient registry of the Department of Neurosurgery, University of Zurich provides extensive population-based data. In this study we focused on complications after lumbar spine surgeries and rated their severity by Clavien-Dindo grade (CDG). Analyzing 138 consecutive surgeries we noted 44 complications. As to treatment, CDG correlated with the length of hospital stay and treatment cost. As to patient outcome, CDG correlated with performance and outcome (McCormick). The present study demonstrates the correlation between outcome scales and the CDG. While the high correlation of CDG with costs seems self-evident, the present study shows this correlation for the first time. Furthermore, the study validates the CDG for a surgical subspecialty. As a further advantage, CDG registers any deviation from the normal postoperative course and allows comparison between surgical specialties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".