Frequency and Acceptability of Adverse Events After Anterior Cervical Discectomy and Fusion
Bibliographic record
Abstract
PURPOSE: Anterior cervical discectomy and fusion has a low but well-established profile of adverse events. The goal of this study was to gauge surgeon opinion regarding the frequency and acceptability of these events. METHODS: A 2-page survey was distributed to attendees at the 2015 Cervical Spine Research Society (CSRS) meeting. Respondents were asked to categorize 18 anterior cervical discectomy and fusion-related adverse events as either: "common and acceptable," "uncommon and acceptable," "uncommon and sometimes acceptable," or "uncommon and unacceptable." Results were compiled to generate the relative frequency of these responses for each complication. Responses for each complication event were also compared between respondents based on practice location (US vs. non-US), primary specialty (orthopedics vs. neurosurgery) and years in practice. RESULTS: Of 150 surveys distributed, 115 responses were received (76.7% response rate), with the majority of respondents found to be US-based (71.3%) orthopedic surgeons (82.6%). Wrong level surgery, esophageal injury, retained drain, and spinal cord injury were considered by most to be unacceptable and uncommon complications. Dysphagia and adjacent segment disease occurred most often, but were deemed acceptable complications. Although surgeon experience and primary specialty had little impact on responses, practice location was found to significantly influence responses for 12 of 18 complications, with non-US surgeons found to categorize events more toward the uncommon and unacceptable end of the spectrum as compared with US surgeons. CONCLUSIONS: These results serve to aid communication and transparency within the field of spine surgery, and will help to inform future quality improvement and best practice initiatives.
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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 | 0.005 | 0.031 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".