Elevated Troponin: A Marker for Noncardiac Complications After Resection of Head and Neck Cancers
Bibliographic record
Abstract
Objective To examine if troponin positivity (TP) in patients who undergo head and neck cancer mucosal resections (HNS) predicts noncardiac complications (NCC). Background Major HNSs are arduous operations that place stress on the patient's hemodynamic system. TP after noncardiac surgery previously has been shown in up to 25% of patients, which may signal cardiac complications (CC) or NCC. Although CC after HNS has been observed, no study has investigated the relationship of TP to NCC. Methods All patients who underwent HNS at a tertiary‐care cancer center from July 2014 to July 2016 were included and underwent postoperative troponin measurements as part of an institutional cardiac protocol. Comparative and multivariate regression analysis were used to compare TP and troponin‐negative (TN) patients for NCC. Results One hundred seventy‐two patients underwent HNS. Of those, 15% developed TP during the postoperative period. There was no significant difference between TP and TN for gender, tumor‐node‐metastasis staging, Charlson comorbidity index, and smoking status. Risk of NCC in TP was 73.1% versus 28.1% in TN (P < 0.001). A significant difference (P < 0.05) in wound complications, length of hospital stay (LOHS), and incidence of pneumonia was found between both groups. Nonparametric testing confirmed significant difference in pneumonia (Z = −3.469, P = 0.001) and LOHS (−3.110, P = 0.002). Multivariate regression analysis confirmed a significant difference in LOHS independent of CC (R2 = 0.122, P < 0.0001). Conclusion TP is not an uncommon occurrence after HNS and is associated with statistically significant increases in wound complications, LOHS, and pneumonia. However, the overall significance of these findings remains unclear, and further research is warranted to determine if outcomes may be improved by closely monitoring these patients for TP. Level of Evidence 4 Laryngoscope, 130:2148–2152, 2020
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".