Perceptions and Practices for Thyroid Surgical Reporting: A Canadian National Study
Bibliographic record
Abstract
INTRODUCTION: Information documented in narrative surgical reports influences the management of thyroid disease but often lacks consistency. This study aimed to investigate national perceptions of current thyroid surgical reporting practices and identify frequently reported items. METHODS: Surgeons who perform thyroidectomy were surveyed for their opinions on current surgical reporting practices and asked to provide de-identified narrative reports dictated for benign and/or malignant disease. Survey results and report elements were summarized using descriptive statistics. RESULTS: Forty-two surgeons from 9 Canadian provinces were surveyed with 89% performing >16 thyroidectomies annually. Narrative reporting was used by 88% of surgeons, of whom 35% expressed dissatisfaction and 100% expressed interest in using a synoptic template if available. Those who already used synoptic templates (12%) reported 100% satisfaction (Fig. 1). Among 142 narrative reports evaluated, 76% were dictated for thyroid operation performed for possibly malignant or malignant disease and 30% for benign disease. Essential surgical reporting elements including the status of parathyroid glands (75% to 79%) and recurrent laryngeal nerve(s) (100%) were adequately reported, but disease extent (46%) and tumor size (13%) were not. Additionally, the presence/absence of gross extrathyroidal cancer extension (36%) and the presence of residual cancer (6%) were inconsistently documented in reports for malignant disease. Nonessential elements such as incision placement (100%) were routinely reported.Figure 1.: Summary of current reporting options and surgeon satisfaction.CONCLUSION: Narrative surgical reporting dominates current practice but fails to document important prognostic information. Development of an accepted standardized national synoptic operative template would benefit quality of patient care, improve consistency and patient outcomes, and boost user satisfaction.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".