Bibliographic record
Abstract
Most published clinical research is faulty because of many reasons, one being faulty design. A remedy to this problem is the correct utilization of the PICOT (population, intervention, comparative intervention, outcome and time horizon) format in the design of a clinical research question. One element of the PICOT format, "outcome," has not been assessed adequately in aesthetic surgery. In this review, we found that in the last decade of all randomized controlled trials and comparative studies published in Aesthetic Surgery Journal, only about half specified a primary outcome. Regrettably, only 40% reported both a primary outcome and justification for choosing this outcome. This poses a credibility issue with the conclusions of the majority of published studies. There is an urgent need to develop critical outcome sets for aesthetic procedures to be utilized by future investigators. With such a critical outcome set, we will be able to pool the results of multiple studies on the same subject and reach conclusive results.
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 | 0.238 | 0.517 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.019 | 0.041 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".