Subclinical Peroneal Neuropathy: A Common, Unrecognized, and Preventable Finding Associated With a Recent History of Falling in Hospitalized Patients
Bibliographic record
Abstract
PURPOSE: Identification of modifiable risk factors for falling is paramount in reducing the incidence and morbidity of falling. Peroneal neuropathy with an overt foot drop is a known risk factor for falling, but research into subclinical peroneal neuropathy (SCPN) resulting from compression at the fibular head is lacking. The purpose of our study was to determine the prevalence of SCPN in hospitalized patients and establish whether it is associated with a recent history of falling. METHODS: We conducted a cross-sectional study of 100 medical inpatients at a large academic tertiary care hospital in St Louis, Missouri. General medical inpatients deemed at moderate to high risk for falling were enrolled in the summer of 2013. Patients were examined for findings that suggest peroneal neuropathy, fall risk, and a history of falling. Multivariate logistic regression was used to correlate SCPN with fall risk and a history of falls in the past year. RESULTS: The mean patient age was 53 years (SD = 13 years), and 59 patients (59%) were female. Thirty-one patients had examination findings consistent with SCPN. After accounting for various confounding variables within a multivariate logistic regression model, patients with SCPN were 4.7 times (95% CI, 1.4-15.9) more likely to report having fallen 1 or more times in the past year. CONCLUSIONS: Subclinical peroneal neuropathy is common in medical inpatients and is associated with a recent history of falling. Preventing or identifying SCPN in hospitalized patients provides an opportunity to modify activity and therapy, potentially reducing risk.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".