AQUILA: assessment of quality in lower limb arthroplasty. An expert Delphi consensus for total knee and total hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: In the light of both the importance and large numbers of case series and cohort studies (observational studies) in orthopaedic literature, it is remarkable that there is currently no validated measurement tool to appraise their quality. A Delphi approach was used to develop a checklist for reporting quality, methodological quality and generalizability of case series and cohorts in total hip and total knee arthroplasty with a focus on aseptic loosening. METHODS: A web-based Delphi was conducted consisting of two internal rounds and three external rounds in order to achieve expert consensus on items considered relevant for reporting quality, methodological quality and generalizability. RESULTS: The internal rounds were used to construct a master list. The first external round was completed by 44 experts, 35 of them completed the second external round and 33 of them completed the third external round. Consensus was reached on an 8-item reporting quality checklist, a 6-item methodological checklist and a 22-item generalizability checklist. CONCLUSIONS: Checklist for reporting quality, methodological quality and generalizability for case series and cohorts in total hip and total knee arthroplasty were successfully created through this Delphi. These checklists should improve the accuracy, completeness and quality of case series and cohorts regarding total hip and total knee arthroplasty.
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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 | 0.201 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".