Treatment of Sagittal Band Injuries and Extensor Tendon Subluxation: A Systematic Review
Bibliographic record
Abstract
Background: This systematic review assesses the current literature and reviews the clinical outcomes of treatment for sagittal band injuries and extensor tendon instability. Materials: A systematic search of MEDLINE, EMBASE, and the Cochrane databases was performed for English-language articles on the treatment of nonrheumatoid adult sagittal band injuries between 1969 and 2019. Two independent reviewers were involved in screening, data extraction, and critical appraisal. The level of evidence was assigned using the Sackett scale, and the methodological quality of the studies was evaluated using the Structured Effectiveness Quality Evaluation Scale (SEQES). Outcome measures were persistent pain, extensor lag, and recurrent tendon subluxation. Results: In all, 1653 abstracts were identified, with 43 articles reviewed in full text and 17 articles (429 treated digits) included in the final systematic review. There were 10 studies on surgical management, 3 on nonoperative management, and 4 on both. There were 4 retrospective case series and 13 retrospective case reports (Sackett level 4) with an average SEQES score of 15 (low quality). Studies on nonoperative management had on average more digits per study and higher SEQES scores (n = 27.7, SEQES = 19) compared with studies on surgical management (n = 11.8, SEQES = 13.8). Variability in reported outcome measures precluded meta-analysis. Conclusion: Qualitative synthesis of available literature suggests that acute sagittal band injuries can be successfully treated by splinting the injured digit in neutral or hyperextension. Patients with chronic injuries or those failing nonoperative management may benefit from surgical exploration. A lack of consistent outcome measures precluded comparison of surgical techniques.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".