Evidence map›Paper›PMID 31708661›Full record

ArticleCurrent oncology (Toronto, Ont.)2019

The generation of two specific cancer costing algorithms using Ontario administrative databases.

N Mittmann, S Y Cheng, N Liu, S J Seung, F E Saxena, C DeAngelis, N J Look Hong, C C Earle, M C Cheung, N Leighl and 2 more

Abstract read
In one paragraph

Article in Current oncology (Toronto, Ont.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

N MittmannSunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto.
S Y Chengices, Toronto.
N Liuices, Toronto.
S J SeungHealth Outcomes and PharmacoEconomic (hope) Research Centre, Toronto.
F E Saxenaices, Toronto.
C DeAngelisOdette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto.
N J Look HongOdette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto.
C C EarleSunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto.
M C CheungOdette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto.
N LeighlUniversity Health Network, Toronto.
N Coburnices, Toronto.
W K EvansMcMaster University, Hamilton, ON.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer treatment and management have become increasingly economically burdensome. Consequently, to help with planning health service delivery, it is vital to understand the associated costs. Administrative databases can be used to help understand and generate real-world system-level costs. Using databases to generate costs can take one of two approaches: top-down or bottom-up. Top-down approaches disaggregate the total health care spending from a global health care budget by sector and provider. A bottom-up approach begins with individual-level health care use and its costs, which are then aggregated.

Indexed as

AlgorithmsHealth Care CostsDatabases, FactualHumansNeoplasmsOntarioCosting algorithms

Identifiers

PMID31708661
PMCPMC6821127

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.