PREVENCIJA KARCINOMA GRLIĆA MATERICE
Bibliographic record
Abstract
I pored razvijenih sistema prevencije, ali i savremenih, uspešnih modaliteta lečenja, karcinom grlića materice predstavlja i dalje veliki socijalni i epidemiološki problem u svetu, naročito izražen u nerazvijenim zemljama i zemljama u razvoju. Prema incidenci i mortalitetu od karcinoma grlića materice, Srbija se decenijama unazad nalazi u vrhu liste evropskih zemalja. Dokazana centralna uloga hronične HPV infekcije i perzistentnog prisustva virusne DNK u genetskom materijalu ćelija cervikalne sluznice, dovela je i do razvoja uspešnih mera prevencije. Primarna prevencija je predstavljena kroz HPV imunizaciju populacije u dečijem/ranom adolescentnom dobu, što omogućava smanjenje učestalosti HPV infekcije, a time i smanjenje incidence patoloških promena na sluznici grlića. Sekundarna prevencija je predstavljena kroz skrining sa ciljem otkrivanja asimptomatskih premalignih promena ili ranih stadijuma invazivne bolesti. Pokazano je da se populacioni benefit od vakcinacije postiže kada nivo imunizacije pređe 50%. U Srbiji je trenutno dostupna devetovalentna HPV vakcina, mada sama vakcinacije nije obavezna već je na nivou preporuke, što uz nedovoljnu informisanost o HPV infekciji, udruženo sa već naširoko rasprostranjenom negativnom senzibilizacijom opšte populacije u Srbiji prema vakcinaciji uopšte, daje ukupno nizak procent vakcinisanih. Takođe, još uvek ne postoji adekvatno razvijen organizovani skrining, on se najčešće sprovodi kao povremeni, oportuni skrining, baziran na kolposkopiji sa PAP testiranjem, dok se HPV testiranje sprovodi u znatno manjem obimu, što sve predstavlja razloge visoke incidence i mortaliteta od karcinoma grlića materice u Srbiji.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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; 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".