REVIEW OF PATIENTS WITH PERTHES DISEASE RESPONDING POORLY TO NEAR INFRARED IRRADIATION THERAPY
Bibliographic record
Abstract
Since 1993, we have applied a combination of brace treatment and near infrared irradiation therapy (NIIT) in 23 cases of Perthes disease. We previously reported that in patients in whom this combined therapy was started before the early fragmentation stage, lateral pillar formation through ossification of the lateral femoral head occurred quickly and exacerbation of femoral head deformity was mild, thereby shortening the period required for treatment. However, of the 16 patients who began to receive the combination of brace treatment and near infrared irradiation therapy during the early stage of Perthes disease, 4 developed evident femoral head collapse of varying degrees during treatment and 2 of these 4 were rated as poor according to Mose’s criteria. Collapse occurred 5-6 months after the start of treatment. In the 2 cases rated as poor according to Mose’s criteria, it seemed that weight-bearing and one-month suspension of treatment had also affected the onset of collapse. To improve the outcome of near infrared irradiation therapy, it seems desirable to continue daily application without allowing any intervals to the treatment of one week or longer. Furthermore, it is essential to ensure the correct parameters of the NIIT system and the number of sessions, restrict weight bearing and use a brace carefully during the first 7 months of treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".