Perioperative cardiac investigations for chest pain after parathyroidectomy rarely yield a cardiac diagnosis
Bibliographic record
Abstract
Background: The incidence of adverse perioperative cardiac complications after parathyroidectomy has not been well described. This study aimed to evaluate the incidence of perioperative chest pain and cardiac complications after parathyroidectomy and to evaluate risk factors that may identify patients who are more likely to benefit from a cardiac workup. Methods: We performed a retrospective study of all patients undergoing parathyroidectomy for primary hyperparathyroidism by a single endocrine surgeon at a tertiary endocrine centre between 2011 and 2018. Patient demographics, clinicopathologic variables, operative and postoperative details (reported chest pain, performance of a cardiac workup and new postoperative cardiac diagnosis) were reviewed. Patients with chest pain were compared to those without chest pain using the Fisher exact test and Student t test. Results: Fourteen of 295 patients (4.7%) reported chest pain in the immediate postoperative period. Most patients were investigated with a 12-lead electrocardiogram and troponin (n = 12/14), yet none were diagnosed with a cardiac event. When comparing patients with and without chest pain, there was no significant difference in age, gender, body mass index, presence of cardiovascular risk factors, American Society of Anesthesiologists score or length of surgery. Conclusion: Postoperative chest pain after parathyroidectomy is not an uncommon event and leads to a cardiac workup in most cases; however, the risk of significant postoperative cardiac events is minimal. In the “choosing wisely” era, one should evaluate each patient’s pretest probability of such events and avoid extensive workup in low-risk patients to avoid unnecessary costs to the health care system.
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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".