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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".