Quality improvement lessons from Canadian thyroid and parathyroid surgery legal decisions
Bibliographic record
Abstract
BACKGROUND: This is the first study of Canadian thyroid and parathyroid surgery legal decisions, and the first study of surgical malpractice using the Canadian Legal Information Institute (CanLII) database. The objective was to identify quality improvement opportunities in surgical practice, to increase patient safety and satisfaction. METHODS: Legal decisions relating to thyroid and parathyroid surgery in the CanLII database were screened. Cases were included if a surgeon was listed as applicant or respondent; they related to pre-, intra-, or post-operative management of thyroid or parathyroid disease; and malpractice was alleged. Cases were excluded if surgery was mentioned incidentally or for non-surgical focus. RESULTS: Of the 347 unique legal decisions screened, 14 met inclusion and exclusion criteria. Surgeries occurred between 1976 and 2012, with 13 thyroid surgeries, 1 parathyroidectomy, and 4 mortalities. CONCLUSIONS: Quality improvement lessons include communication, pre-operative patient education and documentation of risks discussed, and in-person assessment of complications.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".