Complications in Spinal Deformity Surgery
Bibliographic record
Abstract
In Brief Study Design. Literature review of complications unrelated directly to surgical skills involved in spinal deformity surgery. Objective. Highlight complications associated with perioperative issues. Summary of Background Data. Complications can arise from mundane events that arise during the operative experience, but are not directly related to surgical skills. Methods. Literature reviews that touches on the more common potential complication events that do not involve direct surgical expertise. Results. The topics of positioning, nutrition, blood loss, comorbidities, OR time, and pulmonary and GI concerns are discussed as basics that could derail a surgical outcome even with an otherwise uneventful surgical technique. The need for vigilance is stressed and the nuances of understanding these are discussed. Conclusion. Mundane events can derail a perfectly executed surgical undertaking. Attention to detail, team work, close monitoring, and checklist type focus will help to improve, focus, and avoid these preventable complications that have nothing to do with direct surgical skills. Literature review of the more common pericomplications unrelated directly to surgical expertise. These include the topics of positioning, blood loss, comorbidities, nutrition, OR time, and pulmonary and GI concerns. This discussion highlights a need for attention to detail, teamwork, close monitoring, and focus regarding these topics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".