The PROCESS 2018 statement: Updating Consensus Preferred Reporting Of CasE Series in Surgery (PROCESS) guidelines
Bibliographic record
Abstract
INTRODUCTION: The PROCESS guidelines were published in 2016 to provide a structure for reporting surgical case series. The PROCESS guidelines have since been widely endorsed by a number of journals. The requirement to report compliance with each item outlined in the PROCESS statement has improved the reporting transparency of case series across a number of surgical specialties. Here, we undertook a new Delphi consensus exercise to update the PROCESS guidelines. METHODS: All members of the previous Delphi group were invited to participate. In addition, researchers, editors, and reviewers who have previously published or reviewed case series with the International Journal of Surgery were invited to collaborate. An online questionnaire was sent to participants asking them to rate their agreement with amendments to each of the 29 items. RESULTS: 140 experts were invited to participate, 56 people agreed to participate, and 45 (80%) recipients completed the survey. There was a high level of agreement amongst the expert group, and unanimous consensus was reached in the first round. All except three proposed items were accepted, and the original guidelines were modified accordingly. CONCLUSION: A modified and improved PROCESS checklist is presented, after a Delphi consensus exercise was completed.
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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 | 0.570 | 0.749 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".