Reporting and interpretation of the functional outcomes after the surgical treatment of disruptions of the pelvic ring
Bibliographic record
Abstract
We performed a systematic review of the literature to evaluate the use and interpretation of generic and disease-specific functional outcome instruments in the reporting of outcome after the surgical treatment of disruptions of the pelvic ring. A total of 28 papers met our inclusion criteria, with eight reporting only generic outcome instruments, 13 reporting only pelvis-specific outcome instruments, and six reporting both. The Short-Form 36 (SF-36) was by far the most commonly used generic outcome instrument, used in 12 papers, with widely variable reporting of scores. The pelvis-specific outcome instruments were used in 19 studies; the Majeed score in ten, Iowa pelvic score in six, Hannover pelvic score in two and the Orlando pelvic score in one. Four sets of authors, all testing construct validity based on correlation with the SF-36, performed psychometric testing of three pelvis-specific instruments (Majeed, IPS and Orlando scores). No testing of responsiveness, content validity, criterion validity, internal consistency or reproducibility was performed. The existing literature in this area is inadequate to inform surgeons or patients in a meaningful way about the functional outcomes of these fractures after fixation.
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 | 0.025 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".