Decisional conflict in patients considering diagnostic thyroidectomy with indeterminate fine needle aspirate cytopathology
Bibliographic record
Abstract
BACKGROUND: Fine needle aspiration (FNA) cytopathology is the gold standard work-up for thyroid nodules. However, indeterminate lesions are encountered commonly and can lead to difficult treatment decisions. We sought to determine whether patients experienced decisional conflict surrounding management with diagnostic thyroidectomy in the setting of indeterminate FNA results. METHODS: Patients with indeterminate results of thyroid nodule FNA were prospectively enrolled. All consultations were carried out by three otolaryngologists in a consistent manner. After consultation, participants completed a demographics form and the Decisional Conflict Scale (DCS) questionnaire. RESULTS: Thirty-five patients (28 female) between the ages of 30 and 88 years (mean age 54.89) participated. The median total DCS score was 10.94 (interquartile range, 4.69-25.0). Twelve patients (34%) scored at or above 25 on the DCS, indicating clinically significant level of decisional conflict. Patients reported feeling significantly more confident about their decision after the surgical consultation compared to before the consultation (p = 0.00). The total DCS score was significantly negatively correlated with self-reported confidence after the consultation (r = -0.421, p = 0.012). CONCLUSION: Many patients experienced clinically significant decisional conflict when considering thyroidectomy for management of a thyroid nodule with indeterminate cytopathology. Future research should be directed at developing decision support tools for this patient group, and exploring the impact of decisional conflict on health outcomes.
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".