Risk Factors Associated With Reoperative Surgery for Thyroid Malignancies: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To examine various factors associated with an increased risk of reoperation for persistent or recurrent malignant thyroid cancers. STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary academic hospital centers. METHODS: Patients undergoing surgery for thyroid cancer at 2 tertiary academic institutions from 2006 to 2020 were included. Those who underwent a reoperative procedure were compared with patients only requiring 1 procedure. The Pearson chi-square and independent t test were used to compare group data accordingly. Furthermore, a binomial logistic regression was performed, while machine learning models were used to construct a predictive algorithm. RESULTS: This study included 2266 patients with surgically managed thyroid malignancy, of which 54 (2.4%) necessitated reoperations. Those requiring a second surgical procedure were more likely to be male (40.7% vs 20.9%, P < .001), undergo bilateral (24.1% vs 3.3%, P < .001) and lateral (16.7% vs 1.8%, P < .001) neck dissections, and have a greater number of metastatic lymph nodes (mean, 9.1 vs 3.5; P < .001) and a larger tumor size (mean, 3.0 vs 2.0 cm; P < .001). According to the binomial logistic regression model, lateral neck dissection, greater number of metastatic lymph nodes, and larger tumor size significantly increased the odds of necessitating a second procedure by 7.8 (95% CI, 2.523-24.083), 1.1 (95% CI, 1.032-1.152), and 1.3 (95% CI, 1.064-1.559), respectively. Last, machine learning models could not significantly predict the occurrence of reoperation. CONCLUSION: This study identified patient- and cancer-related characteristics associated with an increased risk of requiring reoperation for thyroid malignancies.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".