The Identification of Intraoperative Risk Factors Can Reduce, but Not Exclude, the Need for Completion Thyroidectomy in Low-Risk Papillary Thyroid Cancer Patients
Bibliographic record
Abstract
Background: The extent of initial surgical resection for low-risk papillary thyroid cancer (PTC) remains debatable. Since the 2015 American Thyroid Association (ATA) guidelines, several retrospective studies have reported that 40–60% of patients initially treated with lobectomy would require a completion thyroidectomy (CTx) due to high-risk pathological features (HRFs). These studies are limited by variable preoperative stratification and inability to quantify the value of intraoperative assessment. The study objectives were to determine whether diligent preoperative and intraoperative assessment improves the appropriateness of initial surgery for low-risk PTCs and whether varying the criteria for lobectomy reduces the need for CTx. Methods: A prospectively collected province-wide database was analyzed over a 10-year period (2008–2017) for patients who underwent a total thyroidectomy (TT) for PTC without preoperative HRFs. All patients had preoperative ultrasound and fine-needle aspirates. Unique to this database are mandatory synoptic operative fields that identify intraoperative risk factors such as positive lymph nodes and local invasion. Results: In total, 74% of patients (709/959) were deemed eligible for lobectomy. Of those eligible, 149 (21%) had intraoperative risk factors that would necessitate conversion to TT at the initial operation. A further 209 (30%) would require CTx due to HRFs on final pathology. Varying the preoperative criteria for lobectomy did not significantly affect intraoperative conversion or CTx rates. Conclusions: Although intraoperative assessment reduced the need for CTx in 21%, up to 30% of patients would still require a second operation. Altering the preoperative criteria does not influence this outcome. Patients deemed eligible for lobectomy should be informed that despite careful pre- and intraoperative assessment, there is up to a 30% risk of requiring CTx.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".