CORRIGENDUM: INVISIBLE STRUGGLES: EXPLORING CHALLENGES FACED BY WOMEN WITH AMPUTATION IN INDIA
Bibliographic record
Abstract
This corrigendum corrects a reference mis-citation in the article "Invisible struggles: Exploring challenges faced by women with amputation in India" (Can Prosthet Orthot J 2024;7(1):5. https://doi.org/10.33137/cpoj.v7i1.44002 CORRIGENDUMINVISIBLE STRUGGLES: EXPLORING CHALLENGES FACED BY WOMEN WITH AMPUTATION IN INDIA In the article mentioned above¹, published in Volume 7, Issue 1, 2024, the authors cited Mishra et al. (2020)² for the following statement: “The 2019 Global Burden of Diseases (GBD) report highlighted that India sees about 23,500 new cases of people with amputation each year, with men making up the majority—around 20,200—while approximately 3,300 are women.” These statistics were incorrectly attributed to the 2019 Global Burden of Diseases (GBD) report and were originally reported in Mohan D (1986).³ These figures do not represent current national statistics. How to cite: Corrigendum: Invisible struggles: Exploring challenges faced by women with amputation in India. Can Prosthet Orthot J. 2025; Volume 8, Issue 2, No. 5. https://doi.org/10.33137/cpoj.v8i2.46384
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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 | 0.003 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.076 | 0.041 |
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".