Impact of defect size and bone or tendon exposure on outcomes of artificial dermis repair for finger defects
Bibliographic record
Abstract
OBJECTIVE: To examine the outcomes of artificial dermis in repairing finger pulp defects of varying sizes, with a focus on how defect size and exposure of bone or tendon influence sensory recovery, scarring, and other clinical results. METHODS: artificial dermis in 30 patients (36 fingers) treated between June 2022 and October 2023. Outcomes included sensory recovery (via Semmes-Weinstein Monofilament [SWM] and two-point discrimination [2-PD]), scarring (Vancouver Scar Scale [VSS)]), pain, and cold intolerance. Linear regressions were used to analyze associations between defect characteristics and recovery. RESULTS: Over a mean follow-up of 15.7 months, 14 patients reported residual numbness, with mean 2-PD of 5.9 mm and SWM of 3.87. All patients developed scars; 20 fingers showed tissue thinning, 14 had nail deformities, and 1 developed a flexion contracture. Larger defects were linked to worse 2-PD and higher VSS scores, and exposure of bone or tendon was associated with poorer sensory recovery. CONCLUSION: Artificial dermis offers reliable coverage for finger pulp defects but shows clear limitations in larger defects and those with bone or tendon exposure, where sensory recovery and aesthetic outcomes are compromised. These findings underscore the need for cautious patient selection and further comparative studies.
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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".