Reconstruction after Talar Tumor Resection: A Systematic Review
Bibliographic record
Abstract
This systematic review investigated the functional outcomes and complications of reconstruction methods after talar tumor resection. A systematic search of PubMed, Embase, and the Cochrane Central Register of Controlled Trials databases identified 156 studies, of which 20 (23 patients) were ultimately included. The mean Musculoskeletal Tumor Society scores in the groups reconstructed using tibiocalcaneal fusion (n = 17), frozen autograft (n = 1), and talar prosthesis (n = 5) were 77.6 (range 66–90), 70, and 90 (range 87–93), respectively. Regarding complications, sensory deficits were observed in one patient (6%) and venous thrombosis in two patients (12%) in the tibiocalcaneal fusion group, while osteoarthritis was observed in one patient (100%) in the frozen autograft group. No complications were observed in the talar prosthesis group. Reconstruction with talar prosthesis seems preferable to conventional tibiocalcaneal fusion after talar tumor resection because it offers better function and fewer complications. However, as this systematic review included only retrospective studies with a small number of patients, its results require re-evaluation in future randomized controlled trials with larger numbers of patients.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".