Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: To systematically review the outcomes and complications of cosmetic stature lengthening. METHODS: PubMed and Embase were searched on 10 November 2019 by three reviewers independently, and all relevant studies in English published up to that date were considered based on predetermined inclusion/exclusion criteria. The search was done using "cosmetic lengthening" and "stature lengthening" as key terms. The Preferred Reporting Item for Systematic Reviews and Meta-Analyses statement was used to screen the articles. RESULTS: A total of 11 studies including 795 patients were included. The techniques used in the majority of the patients were classic 3- or 4-ring Ilizarov fixator (267 patients; 33.6%) and lengthening over nail (LON) (253 patients; 31.8%), while implantable lengthening nail (ILN) was used in the smallest number of patients (63 patients; 7.9%). Mean end lengthening achieved was 6.7 cm (SD 0.6; 1.5 to 13.0), and the mean follow-up duration was 4.9 years (SD 2.1; 41 days to 7 years). Overall, the mean number of problems, obstacles, and complications per patient was 0.78 (SD 0.5), 0.94 (SD 1.0), and 0.15 (SD 0.2), respectively. The most common problem and obstacle was ankle equinus deformity, while the most common complications were deformation of the regenerate after end of treatment and subtalar joint stiffness/deformity. CONCLUSION: 2020;9(7):341-350.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it