Hals-Nasen-Ohrenheilkunde in Kenia - Auswirkungen demographischer Parameter
Bibliographic record
Abstract
BACKGROUND: In the time of evidence based medicine the analysis of the influence of demographic parameters and different environmental factors on the treatment concepts in a country is often neglected. This is also true for Otorhinolaryngology. METHOD: An evaluation of the situation concerning distribution of physicians, diagnostic procedures and epidemiology in Kenya has been performed. These factors are discussed in consideration of their effect on the incidence of different diseases and their treatment under the specific socio-economic conditions for the otolaryngological situation in Kenya. RESULTS: In Kenya 28 otolaryngologists are registered that concentrate on few urban regions. Chronic otitis media, malignant tumors in the head and neck region and AIDS associated diseases have meanwhile increased dramatically. Numerous instruments and equipment for diagnosis are missing. Bigger equipment for CT scans are nearly exclusively used by private hospitals. PERSPECTIVE: Beside a better provision with different equipment for diagnosis it is especially the organization of certain training programmes where local physicians are further educated that may lead to an optimised medical care in Kenya.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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 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".