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Record W4415352542 · doi:10.1097/corr.0000000000003733

What Is the Probability of Radial Nerve Recovery After Surgical Repair of Humerus Fractures Accounting for Time Since Injury?

2025· article· en· W4415352542 on OpenAlexaff
Nienke A. Krijnen, Alexander J. Comerci, Linden K. Head, Ingmar W.F. Legerstee, Huub H. de Klerk, Neal C. Chen, Teun Teunis

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2025
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsHumerusRadial nerveOrthopedic surgeryUpper limbMEDLINENerve injury

Abstract

fetched live from OpenAlex

BACKGROUND: Radial nerve injury after operative treatment of humeral shaft fracture usually improves, but a subgroup of patients does not recover. Understanding the probability of recovery over time is important in deciding on watchful waiting, nerve exploration, or tendon or nerve transfer. Bayesian analysis is helpful in estimating such probabilities. This type of analysis is predicated on estimating the probability that an event will occur and subsequently updating that estimate as new information becomes available-for instance, when nerve recovery does not occur over time. QUESTIONS/PURPOSES: Using Bayesian methods applied to a previously studied cohort, we asked: (1) Over time, up to the end of 18 months, what is the probability of radial nerve motor recovery after surgical fixation of humerus fractures? (2) What variables are associated with the timing of radial nerve recovery? METHODS: Between January 2002 and November 2014, we treated 375 patients with open reduction and internal fixation (ORIF) for a traumatic diaphyseal humeral fracture at six urban hospitals (two Level 1 trauma centers, two Level 2 trauma centers, and two community hospitals). From this previously studied cohort, we lost access to one hospital's data, leaving us with 295 records to screen for eligibility. We considered patients with an isolated preoperative or postoperative radial nerve palsy, defined as an inability to extend the wrist against gravity (Medical Research Council grade < 3), as potentially eligible. Based on that, 24% (72 of 295) were eligible; of those, 2.8% (2 of 72) were excluded because a nerve disruption was repaired during ORIF. Another 24% (17) had incomplete data sets, leaving 74% (53) for analysis here. Patients with incomplete data sets did not differ from analyzed patients with respect to demographics or injury characteristics. The median (IQR) age was 43 years (25 to 61), and 49% (26 of 53) of patients were male. Most palsies presented preoperatively (83% [44]). Motor recovery was defined as the ability to extend the wrist against gravity (Medical Research Council grade ≥ 3). We conceptualized the probability of radial nerve recovery over time as two conditional probabilities: (1) the probability that the nerve injury is recoverable (neuropraxia, or recoverable axonotmesis), and (2) the probability that it did not recover at a certain point in time. We used a Bayesian network analysis to model these two probabilities. We based our estimate of the probability of a recoverable nerve injury on the largest systematic review, in which 90% (438 of 488 [95% confidence interval (CI) 87% to 92%]) recovered. To reflect uncertainty, we repeated the analysis for the upper and lower limits of its CI. To estimate the probability that recovery did not happen yet at a certain point in time, we used the timing of recovery from the patients in our cohort. We used Cox proportional hazards analysis to examine associations between time to recovery and demographic and injury variables, including age, sex, fracture type and location, vascular injury, type of fixation, and timing of palsy (preoperative versus postoperative). RESULTS: If a nerve has not recovered by 7 months, the probability of nerve recovery by 18 months was still better than chance, at 56% (range 48% to 62%). If the nerve had not recovered by 1 year, then the probability of recovery was 17% (range 13% to 21%). No variables (such as age, fracture location, vascular injury, or fixation type) were associated with timing of radial nerve recovery. CONCLUSION: Providers can use our findings to counsel patients on the expected probability of nerve recovery, which might reduce anxiety while patients await recovery. These probabilities can aid in the decision whether and when nerve reconstruction, nerve transfers, or tendon transfers may be beneficial. Because the probability of recovery remains relatively high for at least 7 months after injury, early surgery is unlikely to be beneficial in patients with radial nerve motor injury after surgical fixation of humerus fractures. Future studies can provide more specific recovery probabilities by including findings on electrodiagnostic studies and patients treated nonoperatively. LEVEL OF EVIDENCE: Level III, therapeutic study.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.427
Teacher spread0.391 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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