Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".