Studies of surgical outcome after patellar tendinopathy: clinical significance of methodological deficiencies and guidelines for future studies
Bibliographic record
Abstract
Patellar tendinopathy is often treated surgically after failure of conservative treatment but clinical experience suggests that results are not uniformly excellent. The aim of this review was to (i) identify the different surgical techniques that have been reported and compare their success rates, and (ii) critically assess the methodology of studies that have reported surgical outcomes. Twenty-three papers and two abstracts were included in the review. Surgical procedures were categorized and outcomes summarized. Using ten criteria, an overall methodology score was derived for each paper. Criteria for which scores were generally low (indicating methodological deficiency) concerned the type of study, subject selection process and outcome measures. We found a negative correlation between papers' reported success rates and overall methodology scores (r= -0.57, P<0.01). There was a positive correlation between year of publication and overall methodology score (r=0.68, P<0.001). We conclude that study methodology may influence reported surgical outcome. We suggest practical guidelines for improving study design in this area of clinical research, as improved study design would provide clinicians with a more rigorous evidence-base for treating patients who have recalcitrant patellar tendinopathy.
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 | 0.605 | 0.799 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".