Male Sex Is Not a Risk Factor for Prognosis in Postoperative Thyroid Cancer Patients: A Propensity Score Matching Study
Bibliographic record
Abstract
PURPOSE: To study whether male sex is a risk factor for prognosis in patients with differentiated thyroid cancer after 131I treatment using the propensity score matching (PSM) method. METHODS: From April 2016 to October 2021, 1948 postoperative differentiated thyroid cancer patients aged 43 (interquartile range: 34, 54) years who received 131I treatment were retrospectively enrolled and divided into male (n = 645) and female groups (n = 1303). The PSM method was adopted to process all data to reduce the influence of data bias and confounding variables. The Mann-Whitney U test and χ2 test were used for data analysis. Multivariate logistic regression was used to analyze the risk factors affecting prognosis, and the receiver operating characteristic curve was used to analyze the relationship between stimulated thyroglobulin (sTg) level, 131I dose, and poor prognosis. RESULTS: Before PSM, the proportion of male patients with poor prognosis was significantly higher than that of female patients. After PSM, there was no difference in the proportion of poor prognosis between male and female groups. Multivariate logistic regression analysis showed that male sex; high T stage, N1b stage, and M1 stage; high sTg level; and high 131I dose were risk factors for poor prognosis before PSM. After PSM, high T stage, M1 stage, high sTg level, and 131I dose were still risk factors but male sex was no longer a risk factor for poor prognosis. CONCLUSIONS: After the reduction of selection bias by PSM, male sex was no longer a risk factor for prognosis after 131I treatment of differentiated thyroid cancer. In addition, high T stage (T3 + T4 stage), M1 stage, sTg ≥10.15 ng/mL, and 131I dose ≥260 mCi were risk factors for poor prognosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".