Bibliographic record
Abstract
“Make a diagnosis, treat the condition and address the cause.” This simple approach, which applies to every clinical scenario, was taught to me by Dr. Clive Noble, an orthopaedic surgeon, the first President of the South African Sports Medicine Association, and one of my mentors. The “438” cricket game at The Wanderers Stadium, Johannesburg between South Africa and Australia. Australia set a world record target of 434 with Ricky Ponting tearing the South African bowling apart in scoring 164 of 105 balls. Miraculously, South Africa chased down the score eventually scoring 438 thanks to 175 off 111 by Herschelle Gibbs – allegedly batting with a hangover! I was the neutral match doctor and witnessed the ebb-and-flow of emotions in both teams as I moved between the change rooms as well as the ecstasy of the crowd. I hate “blow-your-own-trumpet” questions! Probably the translation of international consensus guidelines into an accessible programme for …
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.161 | 0.075 |
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".