The Effect of Isolated Finger Stiffness on Adjacent Digit Function
Bibliographic record
Abstract
Background: Isolated stiffness in a single finger can affect the function of adjacent digits and decrease overall hand function due to the quadriga phenomenon. This study objectively quantifies the dysfunctional impact of each individual stiff finger upon the remaining digits. Methods: Twenty-five individuals (10 men and 15 women) with a mean age of 31 years (range, 18-58 years) without any upper limb pathology, neuropathy, or systemic illness were recruited. Volar-based finger splints were used to hold individual digits of the dominant hand (24 right and 1 left) sequentially in full extension at the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints. Motion of the remaining 3 nonsplinted digits was assessed using a finger goniometer and linear scale to measure the total active range of motion (TAM) and fingertip-to-distal palmar crease (DPC) distance. TAM before and after splinting for each digit was compared using 1-way analysis of variance (ANOVA). Results: Splinting of any individual finger resulted in a significant reduction in the TAM of all adjacent fingers, regardless of which finger was splinted ( P < .001). Digits immediately adjacent to the splinted finger were more heavily impacted compared with nonadjacent digits. Splinting of the ring finger produced the greatest detriment, with a 26% to 47% reduction in the TAM and a DPC distance greater than 40 mm in a third of participants. The index finger caused the least disturbance to remaining digital motion. Conclusions: Isolated finger stiffness causes a variable degree of dysfunction on adjacent normal digits. This emphasizes the need for a focused and proactive approach to restore full active motion following isolated finger injuries to prevent persistent functional sequelae of the hand.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".