Otorhinolaryngology – not just tonsils and grommets: Insights into the ENT scene in South Africa
Bibliographic record
Abstract
Ear, nose and throat (ENT) surgery may be the oldest surgical specialty, but it certainly has not lost its lustre with age.It encompasses a wide variety of pathology, spanning all age groups regardless of gender, ethnicity or socio-economic status.The field of ENT (otorhinolaryngology) includes a broad range of procedures, often using advanced technology both at the bedside and in theatre.Recent decades have seen the discipline expand to include subspecialties like head and neck surgery, neuro-otology, anterior and lateral skull base surgery, allergology and paediatrics.In South Africa (SA), where it is very apparent that there are economic barriers to diagnosis and treatment, we are faced with the co-existence of the developing and developed world.Health budgets, poor infrastructure and shortage of medical staff are some of the challenges regularly encountered.[1] Furthermore, there are individual clinicians and dedicated units who strive for the best outcomes by pioneering ground-breaking surgeries, regardless of limitations in resources -including head and neck cancer units using free flaps, endoscopic skull-base surgery using neuro-navigation and cochlear implant programmes, to name a few.Almost 20% of general practitioner (GP) visits involve complaints arising from the ears, nose or throat.In the paediatric population, this figure rises to around 50%. [2] As of 31 March 2012, 38 236 medical practitioners are registered with the Health Professions Council of South Africa (HPCSA), of whom only 0.9% are ENT specialists (344 practitioners).[3,4] In addition, almost 70% of the 455 July 2013, Vol.103, No. 7 SAMJ Otorhinolaryngology -not just tonsils and grommets: Insights into the ENT scene in South Africa
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".