Fingertip Regeneration with Stacked Biomaterials and Injectable Platelet-rich Fibrin: An Exploratory Prospective Study
Bibliographic record
Abstract
Introduction: Fingertips with some nail intact are notable for regrowth. Earlier studies using occlusive dressings and biomaterials documented good healing. Aims: To explore the usefulness of single-staged stacking of inner layers of biomaterial, covered with bi-layered biomaterial, and weekly injectable platelet-rich fibrin (iPRF) in regenerating fingertips. Methods: A prospective, longitudinal, open-label, exploratory study was performed from May 2019 through June 2021 on patients presenting with injured fingertips and exposed bone/tendon/hardware, including destroyed nails. Avoiding flap surgeries, both natural collagen-GAG (Integra) and synthetic polyurethane (BTM) biomaterials were used along with iPRF injections. Primary endpoint was complete fingertip coverage by secondary intention. Secondary endpoints were percentages of biomaterial-take and scarring, QuickDASH scores, 2-point discrimination, and patient-reported satisfaction at least 3-months postoperatively. Results: All 11 enrolled patients had exposed bone/tendon/K-wire over their fingertips. Integra was applied in 54.55%, and BTM in 45.45%. Biomaterials ‘took’ completely in all patients. Size of fingertip defects ranged between 2.25-7 cm2, median 3.75cm2. Nails appeared destroyed in 54.6% patients. Out of 10 patients completing this study, 2-point discrimination over regenerated fingertips was almost equivalent to opposite side (Median 4mm, Range 4-6mm). Fingertip appearances measured by 13-point Vancouver Scar Scale were nearly normal (Median 0/13, Range 0-5). Hand functions recovered fully, with QuickDASH scores measuring 0/100 in all patients. Patient-reported satisfaction measured by 1-10 visual analog scale was excellent (Median 9.5, Range 8-10). Conclusion: Single-staged stacking of biomaterials over the destroyed fingertips followed by weekly iPRF injections appeared to regenerate fingertips well, even in severe finger injuries with destroyed nails.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".