The World Privacy Forum First report in a series MEDICAL IDENTITY THEFT: The Information Crime that Can Kill You
Bibliographic record
Abstract
This report discusses the issue of medical identity theft and outlines how it can cause great harm to its victims. The report finds that one of the significant harms a victim may experience is a false entry made to his or her medical history due to the activities of an imposter. Erroneous information in health files can lead and has led to a number of negative consequences for victims. Victims do not have the same recourse and help for recovery from medical identity theft as do victims of financial identity theft. This report analyzes statistics in health care and identity theft, and estimates that approximately a quarter million to a half million individuals have been victims of this crime. The report presents the specific harms of medical identity theft based on analysis of cases, and explains why the falsification of information in victims ’ medical files is one of the crime’s core harms. The report reviews the planned National Health Information Network and why the network may facilitate this crime. The report explains the reasons why medical identity theft is challenging to detect, and discusses the specific ways consumers have discovered they were victims of this crime. Summary of Findings and Recommendations
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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 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".