Notice bibliographique
Résumé
The determinants of delay in diagnosis and treatment of childhood cancer are not well studied. While the impact of delays on outcomes is also not well studied, the effects on parental and child well-being and confidence in the health care system, and on the costs to the health care system, are potentially substantial. The term lag time has been applied to the time interval between the onset of symptoms and diagnosis, an obvious prerequisite for the institution of therapy and for enrolment in clinical trials. Lag time comprises elements related to parental delay in the recognition of symptoms, those related to delay after initiation of first medical contact and those related to delay occasioned within the specialized units that provide care to childhood cancer patients. Delay before reaching these specialized units is particularly relevant from the perspective of primary care practitioners because it may be amenable to modification. Parental delay in the recognition of the significance of symptoms has been correlated with the age of the parent (younger parents had shorter lag times), the age of the child (younger children have shorter lag times) and the ordinal position of the child in the family (first-born children had shorter lag times) (1,2). Geographical distance from treating centres has been shown in Canadian studies to be not significant. Overall, in fact, lag times in childhood cancer in Canada are remarkably short (2,3). Adolescents experience longer lag times, and that delay is further prolonged if the adolescent is referred to an adult cancer centre (3). The biology of the tumour clearly influences lag times: several studies have documented shorter lag times for acute leukemias and Wilms tumour (3–5) than for bone or brain tumours. Indeed, brain tumours appear to have the longest lag times, while within the category of brain tumours, posterior fossa tumours have shorter lag times than other tumours (6). The nonspecific nature of symptoms of both brain and bone tumours may contribute to the longer lag times, and education of primary care providers about the presenting symptoms of these diseases to lower the threshold of suspicion is an important strategy to influence the timeliness of referral. The study by Reebye et al (pages 143–147) is an elegant analysis of delays within a specialty centre delivering childhood cancer care. The good news is that overall, the delays identified still resulted in remarkably efficient care – in terms of both timeliness and accuracy. The median time from admission to diagnostic biopsy was one day, and 93% of patients required only one biopsy to achieve a definitive diagnosis. The mean interval between biopsy and pathological confirmation of the diagnosis was one day, with all outliers having legitimate reasons, not attributable to the health care system, for longer waits. The median time from admission to institution of treatment was two days. All of these extremely prompt intervals may have been influenced by the disease distribution in the study population – there was a significant preponderance of acute leukemia (50%), and both brain tumours and bone tumours, disease groups that may have altered the results, were excluded because the primary investigation of these two categories often occurs outside the setting of the Children's Hospital. The bad news is that to achieve this degree of efficiency, all 54 patients required hospital admission, and a substantial proportion of the diagnostic procedures and placement of lines necessary for treatment were undertaken outside of regular working hours, when staffing by both medical and allied health professionals is at a lower level. This was particularly true for nonleukemia patients. Finally, a significant minority of patients (31%) required more than one anesthetic to achieve all necessary procedures. An undefinable but small proportion of these repeat anesthetics were required for legitimate reasons, while many were the result of difficulty coordinating procedures. Whether efficiencies can be achieved by conducting more of the diagnostic workup in an ambulatory context, accommodating out of town patients in hotels or other accommodations, is unclear. Given the case mix, the likelihood is small. Moving the care system away from a system based on ‘goodwill’ and the willingness to undertake after-hours work under less than ideal conditions, however, is a realistic aim. The ability to coordinate procedures, minimize exposure to unnecessary anesthesia and maximize efficiency for a patient population that faces prolonged and repeated aggressive therapies can only be to the health benefit of the patient and the efficiency of the health care system. Across the country, children with cancer are well served by the systematic enrolment in clinical trials that has yielded excellent survival rates, and they could have the experience of cancer made less unpleasant by the adoption of the recommendations of Reebye et al.
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 | 0,009 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».