The Impact of Thyroid Cancer and Post-Surgical Radioactive Iodine Treatment on the Lives of Thyroid Cancer Survivors: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Adjuvant treatment with radioactive iodine (RAI) is often considered in the treatment of well-differentiated thyroid carcinoma (WDTC). We explored the recollections of thyroid cancer survivors on the diagnosis of WDTC, adjuvant radioactive iodine (RAI) treatment, and decision-making related to RAI treatment. Participants provided recommendations for healthcare providers on counseling future patients on adjuvant RAI treatment. METHODS: We conducted three focus group sessions, including WDTC survivors recruited from two Canadian academic hospitals. Participants had a prior history of WDTC that was completely resected at primary surgery and had been offered adjuvant RAI treatment. Open-ended questions were used to generate discussion in the groups. Saturation of major themes was achieved among the groups. FINDINGS: There were 16 participants in the study, twelve of whom were women (75%). All but one participant had received RAI treatment (94%). Participants reported that a thyroid cancer diagnosis was life-changing, resulting in feelings of fear and uncertainty. Some participants felt dismissed as not having a serious disease. Some participants reported receiving conflicting messages from healthcare providers on the appropriateness of adjuvant RAI treatment or insufficient information. If RAI-related side effects occurred, their presence was not legitimized by some healthcare providers. CONCLUSIONS: The diagnosis and treatment of thyroid cancer significantly impacts the lives of survivors. Fear and uncertainty related to a cancer diagnosis, feelings of the diagnosis being dismissed as not serious, conflicting messages about adjuvant RAI treatment, and treatment-related side effects, have been raised as important concerns by thyroid cancer survivors.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".