[et_pb_section fb_built=”1″ module_id=”intro” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”4183fb6d-c74e-4861-9493-5fb6a9ce011c” global_colors_info=”{}”]<\/p>\n
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A new paper on DCS risk assessment is currently sparking debate. It is based on the DAN DSL Database and analyzes 127,957 dives from 5,907 divers, concluding, among other things, that supersaturation on reaching the surface is the most relevant risk factor for DCS. So far, so good\u2014this may not be all that surprising, but it\u2019s useful to be able to back up and quantify the increasing risk with numbers.
The paper has several relevant issues, which we address across three blog posts in total.
1. The analyzed DCS cases are not part of the general data collection. We explain why that\u2019s a problem in the first post.
2. Divers contributed very different profiles. We explain why that is critical for the analysis in another blog post. <\/p>\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”New DAN DB analysis” url=”https:\/\/journals.viamedica.pl\/international_maritime_health\/article\/view\/108038″ url_new_window=”on” use_icon=”on” _builder_version=”4.27.6″ _module_preset=”cd0bdbae-d9d8-4b43-9d1f-98137af1254b” link_option_url=”https:\/\/journals.viamedica.pl\/international_maritime_health\/article\/view\/108038″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n
[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_button button_url=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF9saW5rX3VybF9wb3N0Iiwic2V0dGluZ3MiOnsicG9zdF9pZCI6IjE3MTQ2IiwiZW5hYmxlX2h0bWwiOiJvZmYifX0=@” button_text=”Where the data comes from” _builder_version=”4.27.6″ _dynamic_attributes=”button_url” _module_preset=”1b468ece-fbc6-41f0-9fbb-0d9bc6882bdc” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_button button_url=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF9saW5rX3VybF9wb3N0Iiwic2V0dGluZ3MiOnsicG9zdF9pZCI6IjE2ODk5IiwiZW5hYmxlX2h0bWwiOiJvZmYifX0=@” button_text=”Some divers count more” _builder_version=”4.27.6″ _dynamic_attributes=”button_url” _module_preset=”1b468ece-fbc6-41f0-9fbb-0d9bc6882bdc” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” min_height=”183.5px” custom_margin=”0px|auto|0px|auto|false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
In this third and final part, we look at the one result that is interesting despite all the problems with the data collection: dives that ended with DCS show, on average, higher supersaturation at the end of the dive than those where nothing happened. [\/et_pb_text][et_pb_button _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Surface GF, supersaturation on reaching the surface, Baker GF, or Shearwater GF? Before we take a closer look at the data, we\u2019d like to briefly clarify what is meant by these terms. [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” custom_margin=”||21px|||” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n There is an extremely common mistake in explanations of gradient factors: \u201cGFs are percentages of the M-value\u201d appears in many courses, talks, and explanations in various forms. This will matter later. So remember: gradient factors are fractions of supersaturation\u2014not of the M-value. [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/punkfish-academy.com\/wp-content\/uploads\/2026\/05\/M-line-supersaturation-1.png” title_text=”M-value vs. supersaturation” show_in_lightbox=”on” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_button button_url=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF9saW5rX3VybF9wYWdlIiwic2V0dGluZ3MiOnsicG9zdF9pZCI6IjMwNDgiLCJlbmFibGVfaHRtbCI6Im9mZiJ9fQ==@” button_text=”More on gradient factors” _builder_version=”4.27.6″ _dynamic_attributes=”button_url” _module_preset=”1b468ece-fbc6-41f0-9fbb-0d9bc6882bdc” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” custom_margin=”0px|0px|0px|0px|false|false” custom_padding=”0px|0px|0px|0px|false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”4183fb6d-c74e-4861-9493-5fb6a9ce011c” min_height=”21px” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Back to the question of what the \u201cDSSG\u201d is supposed to be. First, we need to ask whether it\u2019s possible that the DSSG is actually the same value that, in the first analysis, is simply referred to as \u201cGF,\u201d as claimed. The authors of the 2017 analysis of the first part of the database are largely the same as those of the new, expanded analysis of the same database. But the value they use is not compatible between the two studies. [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/punkfish-academy.com\/wp-content\/uploads\/2026\/05\/cialoni-2017-dcs-und-gf.png” alt=”Cialoni et al. 2017 \u2013 Table 4″ title_text=”Cialoni et al. 2017 \u2013 Table 4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font_size=”10px” text_line_height=”1.4em” global_colors_info=”{}”]<\/p>\n from: Cialoni D, Pieri M, Balestra C, Marroni A. Dive Risk Factors, Gas Bubble Formation, and Decompression Illness in Recreational SCUBA Diving: Analysis of DAN Europe DSL Data Base. Front Psychol. 2017 Sep 19;8:1587. doi: 10.3389\/fpsyg.2017.01587. <\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/punkfish-academy.com\/wp-content\/uploads\/2026\/05\/marroni-2026-dcs-dssg.png” alt=”Marroni et al. 2026 \u2013 Table 3 ” title_text=”Marroni et al. 2026 \u2013 Table 3 ” show_in_lightbox=”on” _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font_size=”10px” text_line_height=”1em” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n The first table shows the distribution of the 320 (or 317\u2014three are missing without further explanation) DCS cases across the maximum GF reached during the dive. The symbols are set ambiguously: you would expect <70 to mean all profiles with GFs below 70, including those below 60 or below 50. Apparently, however, it actually means >60; <70, as shown in some other rows. The percentages only add up to 100% in that case. So let\u2019s assume a few symbols were simply placed incorrectly. [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.5″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\n We see that the values reached for DSSG are higher than the values for gradient factors. Exactly how much higher, and exactly what was calculated, we can\u2019t know. Unfortunately, even after repeated inquiries, the authors did not explain it, and no other study provides a plausible explanation of what exactly is calculated as DSSG. They do mention in the paper that not all datasets truly end at the surface\u2014but correcting for that is a very minor issue and clearly cannot lead to such a shift in values. [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
The figures cited here seem quite high: on average, the collected dive profiles ended with a \u201cDSSG\u201d of about 0.74, whereas the DCS dives averaged 0.86. DSSG? That\u2019s the \u201cDAN Surface Supersaturation Gradient.\u201d
What it is isn\u2019t explained\u2014but it refers to Cialoni et al. 2017<\/a> \u2014the previous study analyzing the first nearly 40,000 profiles from the same database. However, the term does not appear there. According to its explanations, that earlier study uses the Surface GF according to Baker<\/a>, as is also commonly used among divers. <\/p>\n<\/div>\nAside: What is the \u201cBaker\u201d surface GF?<\/h3>\n
A gradient factor is a fraction of supersaturation, usually given in %, but sometimes also as a fraction of 1 (GF70 \u2013 70% \u2013 0.7). As a planning setting, gradient factors determine what maximum supersaturation below the limits of the B\u00fchlmann model should be tolerated. When analyzing dive profiles, the gradient factor on reaching the surface\u2014or the maximum GF reached during the dive\u2014can provide an indication of how aggressive or conservative the profile was. <\/p>\n
The M-value is made up of ambient pressure and the tolerated supersaturation at that ambient pressure. Baker also called the supersaturation component the \u201cM-Value Gradient\u201d\u2014which may have contributed to today\u2019s widespread confusion. The M-Value Gradient refers to supersaturation, i.e., the M-value minus ambient pressure. And gradient factors are fractions of that supersaturation. <\/p>\nDifference between the first and second analysis<\/h3>\n
This becomes apparent in how DCS cases are assigned to GF\u2014or to DSSG. Let\u2019s put the two analyses side by side. <\/p>\nWhat\u2019s broken here?<\/h3>\n
In the second, current table there are much more relevant inconsistencies. For one, all 136,793 profiles suddenly appear, even though about 8,000 of them were supposedly filtered out. But things get really wild in the P(DCS) column. The case counts and dive profiles allow a direct calculation of the univariate DCS rate per DSSG class. However, the results in the paper\u2019s P(DCS) column deviate massively from that. In particular, in the DSSG \u22651 class, 45 DCS cases out of 963 profiles yields a rate of about 4.67%, not 37.532%. The reported P(DCS) column therefore cannot be reproduced from the published table values and should not be interpreted as the DCS probability per DSSG class\u2014whatever it is supposed to represent, it is not a calculated probability from the available data. Quite apart from the fact that it is a major error to treat the DCS cases in this dataset as part of the collected dive profiles…
But leaving that aside, we want to know whether it\u2019s possible that GF and DSSG mean the same thing. And we can clearly answer no with a look at the two tables: while in 2017 there were still 59 DCS cases in total with GFs below 70, in 2026 there are only up to 29 left. \u201cUp to\u201d because \u201c0.7\u201d does not clearly describe whether it means 0.65\u20130.75 or 0.6\u20130.7 or perhaps something else… In any case, a significant number of cases would have had to simply disappear. And conversely, it\u2019s the other way around at the higher values: in 2017 there were only 22 cases with GFs above 90; in 2026 there are already 392. But only 308 cases were added…
So the values in the two analyses are clearly not compatible. So what could the \u201cDSSG\u201d be, if it isn\u2019t the GF? <\/p>\nWhat could have happened?<\/h4>\n
So what would be mathematically possible? And what values does DAN use in its public tools? <\/p>\n