Frequency of Non–Cancer-Related Pain in Patients With Cancer
Notice bibliographique
Résumé
TO THEEDITOR: In 2012, theJournal published an article of ours evaluating opioid prescriptions for elderly patients with cancer who reported pain. 1 Pain scores were captured as part of a provincial initiative to screen patients with cancer for symptoms at every visit to the cancer center. 2,3 The study found that 45% of patients with a pain severity score of 4 to 10 out of a possible 10 did not receive opioid analgesics. Possible explanations that have been raised for this observation include non– cancer-related pain and patient refusal to accept opioids. In that same year, the provincial cancer agency undertook a province-wide chart audit to evaluate the documentation at the time of the high pain score, including questions about pain etiology and interventions. Eleven cancer centers audited approximately 25 charts from each patient who reported a pain score of at least 4, for a total audit of 299 charts. Of these, 8% clearly documented that the pain was not related to cancer. An additional 5% of patients indicated that the pain was chronic, and 2% indicated that the pain was being managed in the community. It is uncertain if these 7% had cancer-related pain. The estimate would then be that 8% to 15% of the audited cohort with pain scores of 4 to 10 had non– cancer-related pain. Only two patients had documentation that they declined opioids. In the original study, 9,826 patients had pain scores of 4 to 10. Applying the observations above, 786 to 1,474 patients had pain unrelated to cancer. If none of these patients received opioids and they are removed from the denominator, then the proportion of untreated cancer-related pain improves to 35% to 40%, suggesting that pain is still undertreated in a significant proportion of patients with cancer. The observations from the audit are limited because the criteria for chart selection may not have been applied uniformly at each center, and chart abstractors were not centrally trained. There may also be ambiguity in medical charts around the possible reasons for lack of opioid treatment. However, the data resulting from the audit suggest that non– cancer-related pain does not appear to make up a significant proportion of the pain being described by patients with cancer. Additional work is required to better understand reasons for undertreatment of pain in Ontario to facilitate possible interventions to improve patient care.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 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 | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».