Bibliographic record
Abstract
A cure to prevent scoliosis from developing does not seem to be available in the near future. Primarily this is because of a lack of understanding of the aetiology of this devastating disease or cosmetic deformity. While extensive research has been performed in this area over the past 100 years many experiments have been poorly designed because they have been developed on the premise that patients with AIS all have the same, single underlying cause despite much evidence to the contrary. Consequently, much of the data in the literature can be challenged and perhaps explains the lack of significant progress. Certainly, the results from this previous research suggest strongly that a new approach needs to be adopted or the same confusing results will continue to be collected and little progress will be made. There are certain areas of research that hold the greatest potential for success in finding a cure. These are identified in this paper and included in a theoretical research laboratory. It is suggested that this laboratory need not be theoretical if modern, cheap communication systems were readily adopted throughout the world and if people were willing to share ideas readily and contact each other regularly. In perhaps an unconventional way, the emphasis of this paper is on finding a cure to prevent scoliosis from developing and uses the area of research into the aetiology of scoliosis as the platform for discussion.
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 | 0.034 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".