Antipersonnel Land Mines: A Vector for Human Suffering
Bibliographic record
Abstract
Editorials6 March 2001Antipersonnel Land Mines: A Vector for Human SufferingJames C. Cobey, MD, MPH and Nathaniel A. Raymond, BAJames C. Cobey, MD, MPHDr. Cobey and Mr. Raymond: Physicians for Human Rights; Boston, MA 02116Search for more papers by this author and Nathaniel A. Raymond, BADr. Cobey and Mr. Raymond: Physicians for Human Rights; Boston, MA 02116Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-134-5-200103060-00015 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Antipersonnel land mines are an epidemic afflicting the world's poorest, war-wrecked nations, maiming and killing scores of civilians each year. Sleeping in the soil of over 88 countries, these mines are deadly remnants of past and present armed conflict (1). Not only do they kill or injure more than 2000 people per month, their very presence stymies efforts by developing nations to reclaim land for industry and agriculture.Although antipersonnel land mines date back to at least the U.S. Civil War, their use did not become widespread until World War II (2). In the 1960s, the United States, which used ...References1. Landmine Monitor 1999 Report. International Campaign to Ban Landmines. New York: Human Rights Watch; 1999:15. Google Scholar2. Cobey J, Stover E, Fine J. Civilian injuries due to war mines. Techniques in Orthopaedics. 1995;10:259-64. CrossrefGoogle Scholar3. Stover E, McGrath R. Land Mines in Cambodia: The Coward's War. Asia Watch and Physicians for Human Rights. New York: Human Rights Watch; 1991. Google Scholar4. Gray R. War Wounds: Basic Surgical Management. Geneva: International Committee of the Red Cross; 1994:9. Google Scholar5. Stover E, Cobey J, Fine J. The public health effect of land mines: long-term consequences for civilians.. In: Levey BS, Sidel VW, eds. War and Public Health. Ontario: Oxford Univ Pr; 1996:143. Google Scholar6. White J, Rutherford K. The role of the landmine survivor network.. In: Cameron M, Tomlin BW, Lawson RJ, eds. To Walk without Fear. Ontario: Oxford Univ Pr; 1999:106. Google Scholar7. Gard R. The miliary utility of anti-personnel mines.. In: Cameron M, Tomlin BW, Lawson RJ, eds. To Walk without Fear. Ontario: Oxford Univ Pr; 1999:150. Google Scholar Author, Article, and Disclosure InformationAffiliations: Dr. Cobey and Mr. Raymond: Physicians for Human Rights; Boston, MA 02116Corresponding Author: James C. Cobey, MD, MPH, 2029 K Street NW, Washington, DC 20006.Current Author Addresses: Dr. Cobey: 2029 K Street NW, Washington, DC 20006.Mr. Raymond: Physicians for Human Rights, 100 Boylston Street, Suite 702, Boston, MA 02116. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byPotential Applications of Public Health Tools to Bioarchaeological Data Sets: The “Dirty War Index” and the Biological Costs of Armed Conflict for ChildrenEpidemiology of Fatalities and Orthopaedic Trauma in Armed Conflicts and Natural DisastersAn agent–vector–host–environment model for controlling small arms and light weaponsResponsibility for protection of medical workers and facilities in armed conflictLandmine associated injuries in children in TurkeyThe Dirty War Index: A Public Health and Human Rights Tool for Examining and Monitoring Armed Conflict OutcomesHigh-Impact Medical Journals and Peace: A History of InvolvementWar Injuries, Trauma, and Disaster ReliefThe skin and the catastrophes 6 March 2001Volume 134, Issue 5Page: 421-422KeywordsGlobal healthHomicideKneesLong-term carePrevention, policy, and public healthShockSurgeonsTaxesThoraxVaccines ePublished: 6 March 2001 Issue Published: 6 March 2001 Copyright & PermissionsCopyright © 2001 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".