Assessment of Harms Reporting Quality in Randomized Controlled Trials of Aesthetic Rhinoplasty: A Systematic Review
Bibliographic record
Abstract
Abstract Background Rhinoplasty is one of the most common aesthetic plastic surgery procedures. Complications can lead to both aesthetic and functional impairments. The Consolidated Standards of Reporting Trials (CONSORT) Harms statement was developed to promote improved reporting of harm across randomized controlled trials (RCTs). Objectives The aim of this systematic review is to assess harms reporting quality across RCTs on aesthetic rhinoplasty. Methods A literature search was conducted in Ovid MEDLINE and Embase databases (January 1, 2005 to August 4, 2023). RCTs which compared 2 or more interventions in rhinoplasty with primarily aesthetic indications and assessed patient-important outcomes were included. The reporting quality was assessed by following a 40-item checklist endorsed by the 2022 CONSORT Harms Extension update. Results A total of 58 RCTs met the inclusion criteria. Fifteen RCTs addressed harms of treatment in some capacity. Overall, the reporting quality across RCTs was poor, with a median CONSORT Harms score of 33% (range, 16%-83%). A reporting adherence of ≥50% was met by only 8 studies. There was no significant difference in reporting adherence between studies based on journal endorsement of CONSORT or industry vs nonindustry funding sources (P > .05). A high journal impact factor was significantly associated with a higher reporting quality (P = .044). Conclusions CONSORT Harms reporting adherence was poor across the majority of included RCTs. Future trials on aesthetic rhinoplasty should aim to follow the reporting recommendations endorsed by the CONSORT Harms statement to increase transparency and minimize heterogeneity in harms reporting across studies. Level of Evidence: 1
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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 | 0.518 | 0.755 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.029 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".