Reviewing Evidence and Patient Outcomes of Cheilectomy for Hallux Rigidus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Cheilectomy is a joint-sparing surgery for the treatment of moderate stages of Hallux Rigidus (HR). The purpose of this systematic review was to assess the clinical outcomes, range of motion (ROM), complications, and revision rates associated with cheilectomy. Methods: A literature search of the PubMed, Scopus, and Cochrane databases was performed. PRISMA guidelines were used. Risk of bias was assessed through the Newcastle–Ottawa Scale. Meta-analysis of the clinical outcomes scores was performed. Results: The initial search identified 317 articles, with 16 included. Cheilectomy improved ROM by 51.15% (41.23° to 62.32°), with greater gains in traditional (67.72%) vs. minimally invasive (48.74%) techniques. VAS decreased by 72.61%, more in traditional (79.35%) than minimally invasive (64.97%). AOFAS improved by 33.99%, from 61.83 to 82.85. Complications occurred in 11% (11.68% traditional, 9.73% minimally invasive), with residual pain (7.46%) more common in traditional and nerve injury (3.78%) in minimally invasive procedures. Revision rates were 7.4% overall (6.1% traditional, 8.8% minimally invasive). Conclusions: This procedure showed satisfactory results regardless of whether the traditional or minimally invasive technique is used. Current evidence does not allow for a definitive indication, but careful patient selection is advisable, particularly for mild to moderate cases.
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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".