Long-Term Health-Related Quality of Life Outcomes in Digital Replantation versus Revision Amputation
Bibliographic record
Abstract
Background Earlier, digit viability judged the success of digital replantation. Now, utility health-related quality of life (HRQOL) measures can better assess the impact of digital replantation. Methods Overall, 264 digital injury patients were sent a regimen of utility measures: Disabilities of the Arm, Shoulder and Hand (DASH) score, European Quality of Life 5 Dimensions, visual analog scale (VAS), time trade-off (TTO), and standard gamble (SG). Overall, 51 patients responded completely to all of these—36 replantation patients and 15 revision amputation patients. The utility results of these patients were stratified between replantation versus revision amputation; dominant hand replantation versus nondominant hand replantation; and dominant hand revision amputation versus nondominant hand revision amputation. Results The mean VAS score of replant (0.84) and revision amputation (0.75) groups was significantly different (p = 0.05). The mean DASH score of dominant hand replantations (29.72) and nondominant hand replantations (17.97) was significantly different (p = 0.027). The dominant hand revision amputation had higher anxiety levels in comparison to nondominant hand revision amputation (p = 0.027). Patients with two or more digits replanted showed a significant decrease in VAS, TTO, and SG scores in comparison to patients who only had one digit replanted (p = 0.009, 0.001, and 0.001, respectively). Conclusions This study suggests that HRQOL can offer better indices for outcomes of digital replantation. This shows some specific replantation cohorts have a significantly better quality of life when compared with their specific correlating revision amputation cohort. These findings can be employed to further refine indications and contraindications to replantation and help predict the quality of life outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".