HIP FRACTURE SURGERY: WHO SHOULD GO FIRST? A PERSONALIZED MEDICINE TOOL
Bibliographic record
Abstract
In a resource limited environment, clinicians need to prioritize care. Identifying who would most benefit from care, specifically early care, could inform this decision. We introduce a new way to identify patients who will benefit the most when deciding who should be treated first, in hip fracture cases where timing of surgery matters. We assess the probability that the surgery timing is a necessary and sufficient cause for reduction of in-hospital mortality. This approach, Unit Selection based on counterfactual logic developed by Mueller and Pearl, provides a deeper understanding of individual benefits compared to traditional risk assessment tools. We studied hospital records of patient undergoing hip fracture in Canada over 8 years, using the CIHI Discharge Abstract Database. First, we compared the effect of having surgery within two days to waiting longer on 64 groups (strata) of patients with different health, age, hospital, and care factors. Using a Unit Selection approach, we estimated the probability of benefit (decreased probability of mortality), or how likely each group was to benefit from early surgery. We measured the benefit for each person by comparing their potential outcomes after early and delayed surgery. In a cohort of 139,119 patients (74.3% women, 45.8% 85 years or older, 67% receiving early surgery -within 2 days), the average effect showed 8 fewer deaths per 1,000 surgeries when treatment was received early, within 2 days. In 14 out of 64 groups there was a much greater benefit from early surgery than the stated average: with their upper bound ranging from 7% to 15%. We identified Pre-hospital place of residence, Age, Type of Surgery (arthroplasty vs fixation) and Care environment (teaching vs community hospital), as important factors that define the population that may benefit from early surgery (Fig 1) Measuring probability of benefit using the Unit Selection method helped identify “who should go first” by looking at how likely it is that an individual patient benefit from early surgery. We created a Personalized Decision Making Tool that compares the individual-level benefit in different groups based on their clinical and care factors. This could assist doctors in determining which patients should receive hip fracture surgery first when prioritization is necessary. For any figures or tables, please contact the authors directly.
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".