Cervical paraspinal skeletal muscle index outperforms frailty indices to predict postoperative adverse events in operable head and neck cancer with microvascular reconstruction
Bibliographic record
Abstract
OBJECTIVE: Sarcopenia is increasingly being recognized as a negative prognostic factor in patients with head and neck cancer (HNC). We associate a sarcopenia biomarker measured radiographically from computed tomography (CT) of the neck to postoperative adverse events in patients with operable HNC. PATIENTS AND METHODS: A prospective cohort of treatment-naïve HNC patients undergoing surgery with microvascular reconstruction was performed. Cervical paraspinal skeletal muscle index (CPSMI) was calculated using preoperative CT neck imaging and adjusted for height and sex. Postoperative adverse events, including Clavien-Dindo Grade 3+ complications and fistula, were recorded within 30-days of the index surgery. Multivariate logistic regression was used to evaluate the association between CPSMI and postoperative complications. The modified frailty index (mFI) and Risk Assessment Index (RAI) were compared with CPSMI outcomes. RESULTS: A total of 127 patients with mucosal HNC were included in the study. The mean age was 60.5 years, and 87 (68.5%) patients were male. Sixty Clavien-Dindo grade 3+ events occurred; 17 patients developed an oro/pharyngocutaneous fistula. Low CPSMI was independently associated with Clavien-Dindo Grade 3+ events (OR 2.80, 95% CI of 1.18-6.99) and fistula (OR of 6.10, 95% CI of 1.53-24.3) when adjusted for multiple factors. CPSMI outperformed the mFI and RAI frailty indices to predict postoperative adverse events (p < .05). CONCLUSION: Low CPSMI is independently associated with postoperative adverse events and outperforms current frailty indices inoperable HNC with microvascular reconstruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".