Health and exposures of United Kingdom Gulf war veterans. Part I: The pattern and extent of ill health
Notice bibliographique
Résumé
OBJECTIVES: To assess the health of United Kingdom Gulf war veterans, to compare their health to that of similar personnel not deployed, to describe patterns of ill health in both groups, and to estimate their extent. METHODS: Main Gulf (n=4795) and validation Gulf (n=4793) cohorts were randomly selected within strata from the population deployed to the Gulf and a non-Gulf cohort (n=4790) from those who were not sent. Seven years after the war subjects completed a questionnaire about their health in the past month, including 95 symptom questions and two manikins on which to shade areas of pain or numbness and tingling. Responses were subjected to a principal component analysis with rotation and to a cluster analysis within each cohort. Mean symptom score was used as a measure of severity. Areas shaded on the manikins were coded to indicate widespread pain and possible toxic neuropathy. RESULTS: A response of 85.5% was achieved. Those who had been to the Gulf were more troubled by every symptom with a mean severity score (3.0) substantially greater than in the non-Gulf cohort (1.7). Seven factors were extracted accounting for 48% of the variance. The scores on five factors (labelled psychological, peripheral, respiratory, gastrointestinal, and concentration) were significantly worse in those who had been to the Gulf. Symptoms suggestive of peripheral neuropathy were found more often (12.5%) in the Gulf than the non-Gulf (6.8%) cohorts. Widespread pain was also found more often (12.2% Gulf; 6.5% non-Gulf). Those who had been to the Gulf were found disproportionately (23.8%) in three clusters with high mean severity scores; only 9.8% of non-Gulf respondents were in these clusters. There was no evidence of an important excess in the use of alcohol, tobacco, or referral to hospital specialists by those who had been to the Gulf. For the same level of reported ill health those who had been to the Gulf were less likely to be referred to specialists than non-Gulf veterans. CONCLUSION: 7 Years after the war, the Gulf war veterans were more troubled about their health than those who had not been sent, with a substantial subgroup reporting a pattern of symptoms suggestive of a significant decline in health.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».