Development and validation of the modified index of fragility in head and neck cancer surgery
Bibliographic record
Abstract
BACKGROUND: This study aims to develop and validate, a clinically useful modified index of fragility (mIFG) to identify patients at risk of fragility and to predict postoperative adverse events. METHOD: An observational study was performed using the American College of Surgeons National Surgical Quality Improvement Program database, from 2006 to 2018. All patients undergoing nonemergency head and neck cancer surgery were included. A seven-item index (mIFG) was developed using variables associated with frailty, cachexia, and sarcopenia, drawn from the literature (weight loss, low body mass index, dyspnea, diabetes, serum albumin, hematocrit, and creatinine). Multivariable logistic regression was used to model the association between mIFG, postoperative adverse events and death. A validation cohort was then used to ascertain the diagnostic accuracy of the mIFG. RESULTS: A total of 23,438 cases were included (16,407 in the derivation group and 7031 in the validation group). There was a total of 4273 postoperative major adverse events (AE) and deaths, 1023 postoperative pulmonary complications and 1721 wound complications. Using the derivation cohort, the 7-item mIFG was independently associated with death, major AEs, pulmonary and wound complications, when controlling for significant covariates. The mIFG predicted death and major adverse events using the validation cohort with an accuracy of 0.70 (95% CI: 0.63-0.76) and 0.64 (95% CI: 0.63-0.66), respectively. The mIFG outperformed the modified Frailty index. CONCLUSION: The modified index of fragility is a reliable and easily accessible tool to predict risk of postoperative adverse events and death in patients undergoing head and neck cancer surgery.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".