What Are Thyroidectomy Patients Really Concerned About?
Bibliographic record
Abstract
OBJECTIVE: To better appreciate perioperative concerns affecting patients considering thyroidectomy and to understand how they may vary according to patient characteristics. STUDY DESIGN: Cross-sectional analysis. SETTING: Tertiary referral center. SUBJECTS AND METHODS: The authors recruited patients scheduled for thyroid surgery at the McGill University Thyroid Cancer Center. A total of 148 patients completed the 18-item Western Surgical Concern Inventory-Thyroid (WSCI-T) questionnaire. Psychometrics of the WSCI-T were assessed through a principal component analysis with varimax rotation and reliability analyses. Independent-samples t tests and 2-tailed Pearson correlations were ran, identifying areas of elevated concerns and their relationship to gender, age, and surgical procedure (total vs hemithyroidectomy). RESULTS: The principal component analysis revealed the presence of 3 domains of presurgical concerns on the WSCI-T:Surgery-Related Concerns, Psychosocial Concerns, and Daily-Living Concerns. Reliability coefficients for the WSCI-T Total and subscales were satisfactory. Responses on the WSCI-T indicated on average a moderate overall level of concerns before thyroidectomy. Surgery-Related Concerns was the highest domain of concerns, followed by Daily-Living and Psychosocial Concerns, respectively. Patients were mainly worried about the nodule being cancerous, experiencing a change in voice, and surgical complications. Areas of minor concern included being judged or treated differently, becoming depressed, and feeling embarrassed. Women had higher overall levels of concern than men did. Although there were no significant differences in overall levels of concern according to age and surgical procedure, differences were noted at a subscale and item level. CONCLUSION: This study establishes a mean that will permit adequate physician counseling and a better management of patients' perioperative worries.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".