Notice bibliographique
Résumé
Table 1 summarizes the recommendations from the AOSpine Knowledge Forum Tumor spine oncology focus issue. The recommendations span a breadth of clinical dilemmas and are the end result of 2 years of concerted effort among dedicated health care professionals. Structured meetings and surveys with experienced oncology clinicians and methodologists integrated with thorough systematic reviews culminated in the 14 articles and their subsequent recommendations. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology1 was used for this process. Although GRADE provides a reliable process for arriving at trustworthy clinical recommendations, the consumer must understand the meaning and effects of the two recommendations (strong and weak) to apply them.TABLE 1: Summary of Spine Oncology Treatment Recommendations From the AOSpine Knowledge Forum TumorTABLE 1 (Continued): Summary of Spine Oncology Treatment Recommendations From the AOSpine Knowledge Forum TumorTABLE 1 (Continued): Summary of Spine Oncology Treatment Recommendations From the AOSpine Knowledge Forum TumorTABLE 1 (Continued): Summary of Spine Oncology Treatment Recommendations From the AOSpine Knowledge Forum TumorTABLE 1 (Continued): Summary of Spine Oncology Treatment Recommendations From the AOSpine Knowledge Forum TumorTABLE 1 (Continued): Summary of Spine Oncology Treatment Recommendations From the AOSpine Knowledge Forum TumorTABLE 1 (Continued): Summary of Spine Oncology Treatment Recommendations From the AOSpine Knowledge Forum TumorA consensus strong recommendation allows clinicians to confidently apply an intervention “to all or almost all the patients in all or almost all the circumstances without thorough (or even cursory) review of the underlying evidence and without a detailed discussion with the patient.”2 An example would be the recommendation of neoadjuvant chemotherapy and en bloc resection for osteosarcoma of the spine. The reader must be aware that strong recommendations are sometimes made in the setting of low or very low quality evidence. This is not intuitive, given that traditional recommendations only considered evidence and not expert opinion or patient values. This is the practical value of GRADE, as it offers direction in a setting of limited evidence. Evidence, of course, is still important. For example, a clinician would place a higher value on a strong recommendation in a setting of high or moderate quality evidence than low or very low quality evidence. The connotation of the word weak, historically may lead clinicians to dismiss a weak recommendation as poor and not worth pursuing. This is a classic misnomer and not the case for the GRADE weak recommendation. A consensus weak recommendation is an endorsement of the intervention, but the magnitude is less and circumstances altered compared with a strong recommendation. Weak recommendations can be applied to most patients, however, not all patients. To initiate a weak recommendation, a clinician considers fundamental variables impacting the strength of the recommendation: the quality of evidence, risk and benefit of the intervention, clinician's experience, patient preferences, and cost-effectiveness. Thus, a weak recommendation becomes a shared decision-making process with the patient often culminating in the values and preference of the patient. These concepts are very germane to decision-making in oncology. An example would be the treatment of Ewing sarcoma of the spine. Radiation and chemotherapy are absolute treatments, but en bloc resection, which probably improves survival and decreases local recurrence, is a high-risk procedure, which could induce significant impairment and harm. Most clinicians would recommend en bloc resection and patients consent to it, but not everyone would. With a clear understanding of the derivation and meaning of a strong and weak recommendation, the application and impact of these recommendations on clinical practice can be appreciated. We are hopeful these recommendations will serve as trustworthy guidelines and aid the reader in real-life decisions around spine oncology management.
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 | 0,036 | 0,130 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,010 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,017 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,032 |
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 ».