Predictors of return-to-work after thyroid cancer surgery based on random forest model: a cross-sectional study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Thyroid cancer (TC) is the most prevalent malignancy among middle-aged and young adults. Many patients will face the challenge of return-to-work (RTW) after TC surgery. If patients cannot return to work successfully, it may affect their social recovery and quality of life. This study used the random forest algorithm to identify the predictors of RTW after TC surgery. METHODS: A cross-sectional study was conducted, encompassing a sample of 242 patients who underwent TC surgery in Zhujiang Hospital of Southern Medical University from April to December 2023. The participants completed questionnaires including the general information questionnaire, the Return-To-Work Self-Efficacy Questionnaire (RTW-SE), the Cancer Fatigue Scale (CFS), and the Vancouver Scar Scale (VSS). In this study, the predictors of RTW after TC surgery were analyzed by univariate analysis, multiple logistic regression, and random forest model (RFM). RESULTS: The final 229 TC patients were included in this study, of which 183 (79.9%) returned to work, of which 46 (20.1%) failed to return to work. The median time of RTW was 30.00(14.00, 33.75) days after TC surgery. The RFM indicated that RTW-SE was a key predictor related to RTW after TC surgery and other predictors were ranked in order of importance as follows: postoperative time, neck scar (NS), medical insurance, complications, and rehabilitation exercise. CONCLUSIONS: 20.1% (46/229) of patients still failed to return to work after TC surgery. Healthcare professionals ought to emphasize the importance of modifiable factors, improving TC patients' RTW-SE, reducing the formation of NS, minimizing the occurrence of complications, and promoting rehabilitation exercise may help to facilitate RTW after TC surgery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it