Opioid screening and urine toxicology results in outpatient oncology palliative medicine.
Notice bibliographique
Résumé
e24068 Background: Opioid misuse is a major public health issue. Given widespread opioid prescribing in cancer patients (pts), screening for potential misuse is critical. There is lack of real-world data on opioid screening and urine toxicology testing in outpatient oncology palliative medicine. Methods: This is a retrospective clinical analysis of adult cancer pts previously consented for a pharmacogenomics specimen collection study between August 2019-March 2020. Pts completing ≥ 1 outpatient palliative medicine visit with at least half undergoing urine toxicology screening (UTS) per standard practice were included. Pt demographics, medication(s), UTS results, symptoms using Edmonton Symptom Assessment Scale, and opioid screening using Screener and Opioid Assessment for Patients with Pain - Short Form (SOAPP-SF) were collected at baseline and follow up visits, if available. The primary endpoint was the frequency and type(s) of non-compliant (NC) UTS. Secondarily, risk factors for NC UTS were evaluated using univariate and multivariate logistic regression. Results: Of 189 pts (632 visits), 113 underwent UTS, 125 SOAPP-SF, and 75 had both. The median age was 56, 56% were female, 58% white, 40% black, 48% had stage IV disease, and median pain score was 7. More black pts (72%) underwent UTS compared to white pts (53%) (p = 0.001). The mean age of pts with a UTS was 53 compared to 59 in those without UTS (p = 0.002). Oxycodone was the most prescribed drug (N = 125). Median SOAPP-SF was 3 (range 0-11); 38% had a score ≥ 4 (considered high risk). About half (54%; N = 61) who underwent a UTS were NC. Of these, 32 had 1 NC UTS, whereas 29 had 2 or more. The most common reason was presence of a substance not prescribed (N = 44 pts and 128 results), whereas 33 pts (53 results) were NC for substance(s) not present but prescribed. Four had presence of marijuana only and 21 with marijuana plus another NC substance; presence of cocaine and alcohol were the 2nd and 3rd most frequent aberrant result. Of those with a NC UTS and SOAPP-SF score (N = 44), 59% had a score ≥ 4. In univariate analyses, SOAPP-SF ≥ 4 (p = 0.004), nausea (p = 0.05), depression (p = 0.02), anxiety (p = 0.01), and prescriptions for antidepressants (p = 0.006), acetaminophen (p = 0.03), and/or dronabinol (p = 0.04), were associated with NC UTS. In multivariate analyses, SOAPP-SF Q4 (use of illegal drugs) (OR 2.86, 95% CI 1.64 to 5.02; p < 0.001) and prescription with muscle relaxants (OR 2.90, 95% CI 1.19 to 7.09; p = 0.019) were associated with increased odds of a NC UTS. Conclusions: About half of those undergoing UTS were NC. SOAPP-SF Q4 and prescription with muscle relaxants were associated with a NC UTS. Overall, pt demographics (e.g. younger, more female, more black patients, severe pain) varied from the typical cancer population. Screening using SOAPP-SF, UTS, pain contracts, prescription drug monitoring databases, and evaluating pt-specific risk factors is important to reduce opioid misuse risk.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».