The evolution of femoroacetabular impingement surgical management as a model for introducing new surgical techniques
Bibliographic record
Abstract
Introducing new surgical techniques and concepts can be difficult. There are many hurdles to overcome initially, such as the learning curve, equipment and technique development, before a standard of care can be established. In the past, new surgical techniques have been developed, and even widely accepted, before any scientific evaluation has been made. At that stage, it may be too late properly to evaluate the effectiveness of treatments, as the objectiveness and/or randomisation process may be obstructed. Since the introduction of evidence-based medicine (EBM), there have been high standards of scientific rigour to prove the efficacy of treatments. Based on the nature of evidence-based acceptance, innovations cannot be subjected to this final process before their evolution process is complete and, as a result, there is a need for the staged scientific development of new surgical techniques that should be adopted. This paper presents a model for this kind of stepwise introduction based on the actual evolution of FAI syndrome surgery. By following a scientific algorithmic methodology, new surgical techniques and concepts can be introduced in a stepwise manner to ensure the evidence-based progression of knowledge.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".