Balancing confidence and conservatism: idiopathic scoliosis in an adult powerlifter
Bibliographic record
Abstract
Growing up I had never given much thought to occasional back pains, nor to the asymmetries of my body, whether I noticed it in my legs, my ribs or my shoulders. Pain was as irritating to me as it might be for anybody else, but without an explanation for the experience, it was easy to not let the sensations overwhelm me. Everybody experiences discomfort every now and again, and mine were certainly not debilitating enough to change anything about my life. One day, while being treated for an unrelated wrist injury, my family doctor noticed some of those asymmetries. While the growing pain had generally stopped—I was 17 now—she referred me for an X-ray, and I obliged. In the follow-up I was told that I had scoliosis, with two curves. I was told that there was a risk of the curves progressing, and that I should take steps to avoid such progression. She referred me to a community physiotherapist, and recommended that I self-monitor my posture. I obliged again, attending physiotherapy three times a week. I also started to avoid engaging in other physical activities. My curves, with hindsight, were not at all severe. But at the time, I grew fearful. My mind …
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".