Kaelavaluga haige käsitluse põhimõtted
Bibliographic record
Abstract
Kaelavalu on levinud tervisehäire. Need haiged on perearstide, närviarstide, ortopeedide, reumatoloogide sagedased külastajad. Enamasti on tegemist hea prognoosiga, sageli iseparaneva mittespetsiifilise kaelavalu või kaelakangusega. Nii äge kui ka krooniline kaelavalu võib olla ka ohtliku halva prognoosiga haiguse sümptomiks. Need seisundid on vaja õigel ajal diagnoosida ja ravida vastava spetsialisti poolt. Valuga kaelas võib kaasneda ka radikulopaatia sümptomaatika. Kõikidel juhtudel, eriti retsidiveeruva, alaägeda ja kroonilise kulu korral on vaja täpsustada vaevuste põhjus. Visualiseerimismeetoditest on eelistatuim MRT. Kaela- ja õlavaluga haige ravi on kompleksne (füsiaatriline ja medikamentoosne). Tähtsal kohal on haige nõustamine, tema aktiivne osavõtt raviprotsessist. Kirurgiline ravi on rakendatav kindlatel näidustustel. Mittespetsiifilise kaelavalu ravis (sh kaelakangus) on esmasteks meetmeteks tavapärase aktiivsuse säilitamine ja adekvaatne valuravi.´ Eesti Arst 2010; 89(2):121−125
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.022 |
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".