A New Classification Scheme for Closed Avulsion Injuries of the Flexor Digitorum Profundus Tendon
Bibliographic record
Abstract
BACKGROUND: Closed avulsion injury of the flexor digitorum profundus (FDP) tendon is a relatively common condition of the hand. Its present classification system seems to be deficient in including all possible patterns of injury, leading to improper selection of the best treatment method for the injury. We aim to provide a new classification scheme for this injury. METHODS: We developed a new classification scheme based on the possible pattern of FDP tendon injury. It consisted of three main types, and 10 sub-types. This was used to classify the injury of 34 patients, and help in choosing the best management approach. All patients underwent surgical treatment of their injury. The distal interphalangeal (DIP) joint extension deficit and total active motion (TAM) of the proximal interphalangeal (PIP) and DIP joints were considered as the main outcomes. Five orthopaedic surgeons used the new classification system for our cases, and the inter-rater reproducibility was tested with Fleiss' kappa. RESULTS: The multi-rater kappa for the classification was excellent. At the final follow-up visit 24 months following surgery, the mean loss of extension of the DIP joint was 13.76° ± 13.53° (range 0° to 45°), while the mean TAM was 148.88° ± 22.64° (range 94° to 172°). Based on the TAM score, 21 (61.76%), 8 (23.53%) and 5 (14.71%) patients had excellent, good and fair results, respectively. None of our patients had poor results. CONCLUSIONS: Our new classification scheme of FDP tendon avulsion appeared to be comprehensive and useful in guiding the surgeon for the best treatment option. Nevertheless, this should be confirmed by using it for larger number of patients with different patterns of injury.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".