Relationship Between Sensibility Tests and Functional Outcomes in Patients With Traumatic Upper Limb Nerve Injuries: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To investigate which tests of hand sensibility correlate with functional outcomes in patients with upper limb traumatic nerve injuries and to assess if composite scales of sensibility correlate with functions. DATA SOURCES: MEDLINE, Embase, and Cochrane Central Register of Controlled Trials were searched in May 2020, with a supplementary search in July 2020. Reference lists of the included publications were hand searched. STUDY SELECTION: Database search found 2437 records. Eligible studies reported on inferential association between sensibility tests and functions pertaining to adults after upper limb nerve repair. Two reviewers independently assessed eligibility. Fifteen publications were included. DATA EXTRACTION: Extracted data contain patient characteristics, surgical procedure, follow-up duration, sensibility tests, and functional assessments. Two reviewers independently assessed data quality. DATA SYNTHESIS: <.05). Medical Research Council sensory scale was related to pick-up test; return to work status; and Disabilities of the Arm, Shoulder, and Hand questionnaire. CONCLUSIONS: Monofilament tests allow practitioners to gather sensibility data meaningful to patients' overall recovery of functions after upper limb nerve trauma. For 2-point discrimination and other sensibility tests, practitioners should be aware that improvement in test performance does not necessarily translate to improved hand function. Findings from the composite scales indicate that hand sensibility, in general, is related to functions. Future research on other common sensibility tests is recommended to explore how the test relates to patients' functions.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".