A Population-Based Study of 30-day Incidence of Ischemic Stroke Following Surgical Neck Dissection
Bibliographic record
Abstract
The objective of this study was to determine the 30-day incidence of ischemic stroke following neck dissection compared to matched patients undergoing non-head and neck surgeries.A surgical dissection of the neck is a common procedure performed for many types of cancer. Whether such dissections increase the risk of ischemic stroke is uncertain.A retrospective cohort study using data from linked administrative and registry databases (1995-2012) in the province of Ontario, Canada was performed. Patients were matched 1-to-1 on age, sex, date of surgery, and comorbidities to patients undergoing non-head and neck surgeries. The primary outcome was ischemic stroke assessed in hospitalized patients using validated database codes.A total of 14,837 patients underwent surgical neck dissection. The 30-day incidence of ischemic stroke following the dissection was 0.7%. This incidence decreased in recent years (1.1% in 1995 to 2000; 0.8% in 2001 to 2006; 0.3% in 2007 to 2012; P for trend <0.0001). The 30-day incidence of ischemic stroke in patients undergoing neck dissection is similar to matched patients undergoing thoracic surgery (0.5%, P = 0.26) and colectomy (0.5%, P = 0.1). Factors independently associated with a higher risk of stroke in 30 days following neck dissection surgery were of age ≥75 years (odds ratio (OR) 1.63, 95% confidence interval (CI) 1.05-2.53), and a history of diabetes (OR 1.60, 95% CI 1.02-2.49), hypertension (OR 2.64, 95% CI 1.64-4.25), or prior stroke (OR 4.06, 95% CI 2.29-7.18).Less than 1% of patients undergoing surgical neck dissection will experience an ischemic stroke in the following 30 days. This incidence of stroke is similar to thoracic surgery and colectomy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".