Bibliographic record
Abstract
Ukladowe i nieukladowe zawroty glowy są czestym problemem u pacjentow w podeszlym wieku. Do najczestszych przyczyn nalezą choroby ucha wewnetrznego oraz ukladu nerwowego (ośrodkowego lub obwodowego). Inne przyczyny zawrotow glowy i zaburzen rownowagi to: choroby ukladu sercowo-naczyniowego, niepoządane dzialania lekow, schorzenia konczyn dolnych, choroby o podlozu psychogennym (psychogenne zawroty glowy) itp. W Dizziness Clinic zbadano 3427 pacjentow w wieku 70 i wiecej lat; przyczyne choroby ustalono w 76,25% przypadkow. Nie stwierdzono szczegolnej postaci zawrotow glowy, ktora bylaby typowa dla chorych w podeszlym wieku. U osob starszych problem ten jest jednak bardziej zlozony niz u mlodych pacjentow, ze wzgledu na ogolne pogorszenie sprawności związane ze starzeniem sie oraz wspolistnienie roznych przyczyn zawrotow glowy. Zatem, podobnie jak w przypadku innych grup wiekowych, przyczyny ukladowych i nieukladowych zawrotow glowy u osob w podeszlym wieku nalezy wnikliwie przeanalizowac. Dla lekarza praktyka najlepszymi narzedziami diagnostycznymi są wywiad, badanie przedmiotowe i obserwacja chorego.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".