Differences in the facial soft tissue thickness depending on the skeletal class and sexLiterature review
Bibliographic record
Abstract
StreszczenieOcena profilu twarzy pacjenta ma istotne znaczenie w diagnostyce oraz planowaniu leczenia ortodontycznego.Tkanki miękkie mogą się znacznie różnić grubością, a przez to maskować występowanie istotnych zaburzeń szkieletowo-zębowych. Cel.Celem pracy było przedstawienie danych z aktualnego piśmiennictwa dotyczących zależności między grubością tkanek miękkich profilu twarzy a klasą szkieletową oraz płcią pacjenta.Materiał i metody.Wykorzystując bazę danych PubMed, wyszukano artykuły na temat istniejących zależności między grubością tkanek miękkich profilu twarzy a klasą szkieletową oraz płcią badanych z lat 2002-2020, z użyciem słów kluczowych: facial soft tissue thickness, malloclusion, skeletal class, facial soft tissue depth.Wyniki.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".