Outcomes After Flexor Tendon Injuries in the Pediatric Population: A 10-Year Retrospective Review
Bibliographic record
Abstract
Background: Pediatric outcomes after flexor tendon repairs are variable, and evidence in the literature remains scarce. Methods: Repair of pediatric flexor tendon injuries was reviewed over a 10-year period (2005-2015). Data collection consisted of patient demographics, injury characteristics, anesthetic choice, repair technique, rehabilitation protocol, American Society for Surgery of the Hand Total Active Motion (TAM) scores, and complications. Results: There were 109 patients included in our study, with a total of 162 digits injured and 235 flexor tendon injuries. The mean age was 12 ± 4.6 years. The small finger (48 of 162; 30%) and the flexor digitorum profundus tendon (126 of 235) were the most commonly injured. The mechanism of injury was mainly from a knife (46 of 109; 42.2%) in zone II (82 of 159; 52%). Injuries were mostly repaired under general anesthetic (61 of 104; 56%). The Kessler technique was the predominant repair mechanism (111 of 225 repairs; 49%). Most patients (103 of 109; 95%) had excellent or good TAM scores with 5 postoperative ruptures reported. The most common complication was stiffness (17 of 121 complications; 14%), with most patients having no complications ( 74 of 109 patients; 68%). Patients were commonly immobilized (mean 8.4 ± 10.3 weeks) with a splint (93 of 109; 85%). There were 85 patients who followed a postoperative rehabilitation protocol for 12 ± 18 weeks. Patient demographics, time of repair, injury characteristics, anesthetic choice, and rehabilitation protocol were not significantly correlated with TAM scores or complication rates. Conclusions: Pediatric tendon injuries have good outcomes with no predictive factors identified. Surgical repairs performed under local anesthetic have similar outcomes without increased rates of complications, but remain underused in the pediatric population.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".