The Economics of Surgical Decision-Making in Geriatric Type II Odontoid Fractures: Reframing the Role of Frailty
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Type II odontoid fractures are among the most common cervical fractures in older adults and are increasing in incidence, yet management remains controversial. Operative fixation may offer earlier reduction in pain and disability but is associated with higher costs and greater morbidity in older and frail patients. We sought to perform a cost-utility analysis of operative vs nonoperative care of type II odontoid fractures in older adults, assessing the impact of age and frailty. METHODS: A lifetime, time-homogeneous Markov model compared operative and nonoperative strategies in patients 65 years or older. Costs (2022 USD) were derived from the US Nationwide Inpatient Sample (2016-2022) and health utilities from Short Form-6 dimension scores in the AO Spine North America geriatric odontoid fracture cohort to estimate quality-adjusted life years (QALYs). Transition probabilities were obtained from a systematic review. Analyses were performed from a healthcare payer perspective to calculate the incremental cost-utility ratio (ICUR). We applied a $100 000/QALY willingness-to-pay threshold to determine cost-effectiveness. Sensitivity analyses assessed robustness. Frailty effects on costs and utilities were modeled with regressions adjusting for the modified frailty index-5 (mFI-5) and incorporated into the model. RESULTS: In the base case of an 81-year-old patient, nonoperative care yielded 4.09 QALYs at $15 840 vs 4.28 QALYs at $40 246 for surgery (ICUR $131 324/QALY). One-way sensitivity analysis demonstrated that operative management was cost-effective below ∼77 years. Incorporating frailty-adjusted costs and utilities shifted this threshold downward to ∼76 years for mFI-5 = 0 and ∼69 for mFI-5 = 5. Probabilistic microsimulations incorporating parameter uncertainty demonstrated robustness of findings in which ICURs rose with both age and frailty. CONCLUSION: In this contemporary cost-utility analysis, both age and frailty were key determinants of surgical value. Increasing frailty lowered the age threshold for cost-effective surgery. Incorporating frailty assessment may improve value-conscious surgical decision-making in geriatric odontoid fractures.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".