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Record W4416499996 · doi:10.1302/1358-992x.2025.14.036

HIP FRACTURE SURGERY: WHO SHOULD GO FIRST? A PERSONALIZED MEDICINE TOOL

2025· article· en· W4416499996 on OpenAlexaffabout
Pierre Guy, Lisa Kuramoto, Mary Dunbar, Boris Sobolev

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHip fractureCohortCounterfactual thinkingSelection (genetic algorithm)MEDLINEReduction (mathematics)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.290
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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