Critically Appraising the Quality of Reporting of American College of Surgeons TQIP Studies in the Era of Large Data Research
Bibliographic record
Abstract
BACKGROUND: The American College of Surgeons-Trauma Quality Improvement Program (ACS-TQIP) database is one of the most widely used databases for trauma research. We aimed to critically appraise the quality of the methodological reporting of ACS-TQIP studies. STUDY DESIGN: The ACS-TQIP bibliography was queried for all studies published between January 2018 and January 2021. The quality of data reporting was assessed using the Strengthening the Reporting of Observational studies in Epidemiology-Reporting of Studies Conducted Using Observational Routinely Collected Health Data (STROBE-RECORD) statement and the JAMA Surgery checklist. Three items from each tool were not applicable and thus excluded. The quality of reporting was compared between high- and low-impact factor (IF) journals (cutoff for high IF is >90th percentile of all surgical journals). RESULTS: A total of 118 eligible studies were included; 12 (10%) were published in high-IF journals. The median (interquartile range) number of criteria fulfilled was 5 (4-6) for the STROBE-RECORD statement (of 10 items) and 5 (5-6) for the JAMA Surgery checklist (of 7 items). Specifically, 73% of studies did not describe the patient population selection process, 61% did not address data cleaning or the implications of missing values, and 76% did not properly state inclusion/exclusion criteria and/or outcome variables. Studies published in high-IF journals had remarkably higher quality of reporting than those in low-IF journals. CONCLUSION: The methodological reporting quality of ACS-TQIP studies remains suboptimal. Future efforts should focus on improving adherence to standard reporting guidelines to mitigate potential bias and improve the reproducibility of published studies.
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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 | 0.740 | 0.939 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.047 | 0.043 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".