Biomarker testing of lung cancer in North America versus globally.
Notice bibliographique
Résumé
8541 Background: Biomarker testing is essential to optimize lung cancer (LC) care, yet uptake of testing is suboptimal due to lack of access, cost, and long turnaround times (TAT). Recent advances now require biomarker testing in early-stage LC. In 2024, the International Association for the Study of Lung Cancer (IASLC) launched a 2 nd global survey to measure improvements and barriers to implementation of testing. We compared results from North America (NA) with global results by high income (HIC) and low or middle income countries (LMIC). Methods: A multi-disciplinary committee of oncologists, pathologists, pulmonologists, epidemiologists, and advocacy partners created the survey. We used mixed methods, with focus groups and in-depth interviews informing the quantitative survey with IRB oversite. Chi-square tests were utilized to compare frequencies between NA v Other HIC (OHIC) and HIC v LMIC. Results: Of the 1677 responses globally, 1501 were from HIC and 176 from LMIC. HIC included 337 responses from NA (287 United States and 50 Canada). Nearly all NA respondents (99%) believe biomarker testing significantly impacts patient outcomes and 94% report a clear understanding of who should be tested (v 91% OHIC, p=0.09). In NA, 66% and 40% ranked biomarker testing as highly important in late- and early- stage LC, respectively (64% and 28% OHIC, p=0.68 and p<0.01). Only 45% of NA respondents were satisfied with biomarker testing conditions (v 52% OHIC, p=0.03), and 69% estimate at least half of LC patients receive biomarker testing (71% OHIC), an increase from 45% in the 2018 survey (p<0.01). We found 40% of respondents from NA sometimes or often began treatment prior to obtaining biomarker results (41% OHIC). Key barriers identified were cost (23%), time (22%), and sample quality (20%), consistent with global and OHIC trends. Mean TAT in NA was 17.1 days (SD 7.8) v 16.1 days (SD 9.0) in HIC. Insufficient tumor was the primary cause for re-biopsy in late and early-stage patients for NA (58%) and HIC (48%). Lastly, 14% of NA reported no additional training in next-generation sequencing beyond medical education (16% OHIC). Globally, conditions were worse in LMIC v HIC including those who sometimes or often begin treatment prior to obtaining biomarker results (73% v 41%, p<0.01) and those who are confident or extremely confident in the adequacy of testing at their institution (48% v 68%, p<0.01). Conclusions: Respondents from NA believe they understand the value of biomarker testing for LC and who should be tested. Testing practices have reportedly improved since 2018, yet less than half of NA respondents are satisfied with biomarker testing practices and many patients are still treated without biomarker information. Responses from NA were similar to OHIC, with some exceptions, but significant disparities were evident in LMIC. We identified key barriers that should be addressed to optimize testing practices and patient outcomes.
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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».