Do Authors Report Surgical Expertise in Open Spine Surgery Related Randomized Controlled Trials? A Systematic Review on Quality of Reporting
Bibliographic record
Abstract
STUDY DESIGN: A systematic review of published trials in orthopedic spine literature. OBJECTIVE: To determine the quality of reporting in open spine surgery randomized controlled trials (RCTs) between 2005 and 2010 with special focus on the reporting of surgical skill or expertise. SUMMARY OF BACKGROUND DATA: In technically demanding procedures such as spine surgery, a surgeon's skill and expertise is expected to play an important role in the outcome of the procedure. To appraise the reported treatment effect of spine surgery related RCTs adequately, any potential skill or experience bias must be reported. METHODS: MEDLINE, the Cochrane Library, and EMBASE were systematically searched for open spine surgery RCTs published between January 1, 2005, and December 31, 2010. Percutaneous techniques were excluded. The quality of reporting of all eligible studies was determined using the checklist to evaluate a report of a nonpharmacological trial. The reporting of surgeons' skill and experience was scored additionally. Subsequently, all authors were surveyed to determine if any information on methodological safeguards was omitted from their reports. All data were analyzed in 2-year time frames. RESULTS: Ninety-nine RCTs were included. Ten studies (10%) described surgical skill or experience, mostly as a description of the learning curve. The majority of publications were unclear about "concealment of treatment allocation" (77%), "blinding of participants" (68%), "blinding of outcome assessors" (77%), and "adhering to the intention-to-treat principle" (67%). Of the 99 surveys, we received 22 (22%) completed questionnaires. In these questionnaires, information about essential methodological safeguards was often available, although not reported in the primary publication. CONCLUSION: This study shows that in open spine surgery RCTs information on skill and experience is scarcely reported. Authors often fail to report essential methodological safeguards. These studies may therefore be prone to expertise bias.
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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 | 0.564 | 0.867 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.021 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.010 | 0.006 |
| 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; 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".